path: root/biblio.bib

######################""
@book{cover1999elements,
  title={Elements of information theory},
  author={Cover, Thomas M},
  year={1999},
  publisher={John Wiley \& Sons}
}

@article{stadler2020synthetic,
  title={Synthetic data-A privacy mirage},
  author={Stadler, Theresa and Oprisanu, Bristena and Troncoso, Carmela},
  journal={arXiv preprint arXiv:2011.07018},
  year={2020},
  publisher={Nov}
}

@inproceedings{abowd2008protective,
  title={How protective are synthetic data?},
  author={Abowd, John M and Vilhuber, Lars},
  booktitle={International Conference on Privacy in Statistical Databases},
  pages={239--246},
  year={2008},
  organization={Springer}
}
@inproceedings{jordon2018pate,
  title={PATE-GAN: Generating synthetic data with differential privacy guarantees},
  author={Jordon, James and Yoon, Jinsung and Van Der Schaar, Mihaela},
  booktitle={International conference on learning representations},
  year={2018}
}
@inproceedings{abay2019privacy,
  title={Privacy preserving synthetic data release using deep learning},
  author={Abay, Nazmiye Ceren and Zhou, Yan and Kantarcioglu, Murat and Thuraisingham, Bhavani and Sweeney, Latanya},
  booktitle={Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10--14, 2018, Proceedings, Part I 18},
  pages={510--526},
  year={2019},
  organization={Springer}
}

@inproceedings{ben2002theoretical,
  title={A theoretical framework for learning from a pool of disparate data sources},
  author={Ben-David, Shai and Gehrke, Johannes and Schuller, Reba},
  booktitle={Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={443--449},
  year={2002}
}

@inproceedings{chen2020differential,
  title={Differential privacy protection against membership inference attack on machine learning for genomic data},
  author={Chen, Junjie and Wang, Wendy Hui and Shi, Xinghua},
  booktitle={BIOCOMPUTING 2021: Proceedings of the Pacific Symposium},
  pages={26--37},
  year={2020},
  organization={World Scientific}
}
@article{rahman2018membership,
  title={Membership inference attack against differentially private deep learning model.},
  author={Rahman, Md Atiqur and Rahman, Tanzila and Lagani{\`e}re, Robert and Mohammed, Noman and Wang, Yang},
  journal={Trans. Data Priv.},
  volume={11},
  number={1},
  pages={61--79},
  year={2018}
}

@article{kivinen1997exponentiated,
  title={Exponentiated gradient versus gradient descent for linear predictors},
  author={Kivinen, Jyrki and Warmuth, Manfred K},
  journal={information and computation},
  volume={132},
  number={1},
  pages={1--63},
  year={1997},
  publisher={Elsevier}
}

@article{breiman2001random,
  title={Random forests},
  author={Breiman, Leo},
  journal={Machine learning},
  volume={45},
  pages={5--32},
  year={2001},
  publisher={Springer}
}

@article{shwartz2022tabular,
  title={Tabular data: Deep learning is not all you need},
  author={Shwartz-Ziv, Ravid and Armon, Amitai},
  journal={Information Fusion},
  volume={81},
  pages={84--90},
  year={2022},
  publisher={Elsevier}
}
@article{grinsztajn2022tree,
  title={Why do tree-based models still outperform deep learning on typical tabular data?},
  author={Grinsztajn, L{\'e}o and Oyallon, Edouard and Varoquaux, Ga{\"e}l},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={507--520},
  year={2022}
}

@ARTICLE{1688199,

  author={Polikar, R.},

  journal={IEEE Circuits and Systems Magazine}, 

  title={Ensemble based systems in decision making}, 

  year={2006},

  volume={6},

  number={3},

  pages={21-45},

  doi={10.1109/MCAS.2006.1688199}}

@INPROCEEDINGS{1626170,

  author={Huang, Y.S. and Suen, C.Y.},

  booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition}, 

  title={The behavior-knowledge space method for combination of multiple classifiers}, 

  year={1993},

  volume={},

  number={},

  pages={347-352},

  doi={10.1109/CVPR.1993.1626170}}



@article{hawkins2004problem,
  title={The problem of overfitting},
  author={Hawkins, Douglas M},
  journal={Journal of chemical information and computer sciences},
  volume={44},
  number={1},
  pages={1--12},
  year={2004},
  publisher={ACS Publications}
}
@inproceedings{ying2019overview,
  title={An overview of overfitting and its solutions},
  author={Ying, Xue},
  booktitle={Journal of physics: Conference series},
  volume={1168},
  pages={022022},
  year={2019},
  organization={IOP Publishing}
}

@misc{stateth,
    titre={Statistiques ethniques},
    howpublished={\url{https://www.insee.fr/fr/information/2108548}},
    note={Dernier accès: 2024-09-19}
}

@article{howard2000race,
  title={Race, socioeconomic status, and cause-specific mortality},
  author={Howard, George and Anderson, Roger T and Russell, Gregory and Howard, Virginia J and Burke, Gregory L},
  journal={Annals of epidemiology},
  volume={10},
  number={4},
  pages={214--223},
  year={2000},
  publisher={Elsevier}
}
@article{williams1996race,
  title={Race/ethnicity and socioeconomic status: measurement and methodological issues},
  author={Williams, David R},
  journal={International Journal of Health Services},
  volume={26},
  number={3},
  pages={483--505},
  year={1996},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}

@article{singler2017roko,
  title={Roko's Basilisk or Pascal's? Thinking of Singularity Thought Experiments as Implicit Religion.},
  author={Singler, Beth},
  journal={Implicit Religion},
  volume={20},
  number={3},
  year={2017}
}


@incollection{green1972race,
  title={Race, social status, and criminal arrest},
  author={Green, Edward R},
  booktitle={Readings in Criminology and Penology},
  pages={267--283},
  year={1972},
  publisher={Columbia University Press}
}
@article{walsh2007psychopathy,
  title={Psychopathy and violent crime: A prospective study of the influence of socioeconomic status and ethnicity},
  author={Walsh, Zach and Kosson, David S},
  journal={Law and human behavior},
  volume={31},
  pages={209--229},
  year={2007},
  publisher={Springer}
}




@inproceedings{pelissier2024privacy,
author = {P\'{e}lissier, Samuel and Aalmoes, Jan and Mishra, Abhishek Kumar and Cunche, Mathieu and Roca, Vincent and Donsez, Didier},
title = {Privacy-Preserving Pseudonyms for LoRaWAN},
year = {2024},
isbn = {9798400705823},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3643833.3656120},
doi = {10.1145/3643833.3656120},
abstract = {LoRaWAN, a widely deployed LPWAN protocol, raises privacy concerns due to metadata exposure, particularly concerning the exploitation of stable device identifiers. For the first time in literature, we propose two privacy-preserving pseudonym schemes tailored for LoRaWAN: resolvable pseudonyms and sequential pseudonyms. We extensively evaluate their performance and applicability through theoretical analysis and simulations based on a large-scale real-world dataset of 71 million messages. We conclude that sequential pseudonyms are the best solution.},
booktitle = {Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks},
pages = {14–19},
numpages = {6},
keywords = {iot, link-layer, lorawan, privacy, pseudonyms},
location = {Seoul, Republic of Korea},
series = {WiSec '24}
}

  


@inproceedings{Lebrun_2022, series={Middleware ’22},
   title={MixNN: protection of federated learning against inference attacks by mixing neural network layers},
   volume={2948},
   url={http://dx.doi.org/10.1145/3528535.3565240},
   DOI={10.1145/3528535.3565240},
   booktitle={Proceedings of the 23rd ACM/IFIP International Middleware Conference},
   publisher={ACM},
   author={Lebrun, Thomas and Boutet, Antoine and Aalmoes, Jan and Baud, Adrien},
   year={2022},
   month=nov, pages={135–147},
   collection={Middleware ’22} }

@article{bergstra2015hyperopt,
  title={Hyperopt: a python library for model selection and hyperparameter optimization},
  author={Bergstra, James and Komer, Brent and Eliasmith, Chris and Yamins, Dan and Cox, David D},
  journal={Computational Science \& Discovery},
  volume={8},
  number={1},
  pages={014008},
  year={2015},
  publisher={IOP Publishing}
}

@misc{iris_53,
  author       = {Fisher, R. A.},
  title        = {{Iris}},
  year         = {1936},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C56C76}
}
@misc{chatgpt,
    title={ChatGPT},
    howpublished={\url{https://openai.com/chatgpt/}},
    note={Dernier accès: 2024-09-19}
}
@misc{stabledi,
    title={Stable Diffusion},
    howpublished={\url{https://stablediffusion.fr/france}},
    note={Dernier accès: 2024-09-19}
}
@inproceedings{maghded2020novel,
  title={A novel AI-enabled framework to diagnose coronavirus COVID-19 using smartphone embedded sensors: design study},
  author={Maghded, Halgurd S and Ghafoor, Kayhan Zrar and Sadiq, Ali Safaa and Curran, Kevin and Rawat, Danda B and Rabie, Khaled},
  booktitle={2020 IEEE 21st international conference on information reuse and integration for data science (IRI)},
  pages={180--187},
  year={2020},
  organization={IEEE}
}



@misc{yeom2018privacy,
      title={Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting}, 
      author={Samuel Yeom and Irene Giacomelli and Matt Fredrikson and Somesh Jha},
      year={2018}
}
#Notes
@misc{dati2024declaration,
    title={Déclaration de Mme Rachida Dati, ministre de la culture, lors de l'installation de la Commission d'enrichissement de la langue française, le 27 mai 2024.}
    author={Dati, Rachida},
    year={2024}
}
@misc{wise2024,
    title={Wise 2024},
    howpublished={\url{wise2024-qatar.com}},
    note={Dernier accès: 2024-09-13}
}

###########################"
#Contexte
#Legal
@misc{defenseur2015emploi,
    title={Recruter avec des outils numériques sans discriminer},
    year={2015},
    howpublished={\url{https://juridique.defenseurdesdroits.fr/doc_num.php?explnum_id=18909}},
    note={Dernier accès: 2024-09-13}
}

@misc{defenseure2024lutter,
    title={Lutter contre les discriminations produites par les algorithmes et l’IA},
    year={2024},
    howpublished={\url{https://www.defenseurdesdroits.fr/sites/default/files/2024-02/FICHE7_AlgoIA_0.pdf}},
    note={Dernier accès: 2024-09-13}
}

@misc{defenseure,
    title={Lutter contre les discriminations et promouvoir l'égalité},
    howpublished={\url{defenseurdesdroits.fr/lutter-contre-les-discriminations-et-promouvoir-legalite-185}},
    note={Dernier accès: 2024-09-13}
}

######################################"
#Background
@BOOK{lecun2019quand,
  title     = "Quand la machine apprend",
  author    = "Le Cun, Yann",
  publisher = "Odile Jacob",
  month     =  oct,
  year      =  2019,
  address   = "Paris, France",
  language  = "fr"
}

#Set
@book{enderton1977elements,
  title={Elements of set theory},
  author={Enderton, Herbert B},
  year={1977},
  publisher={Academic press}
}

#Mesure
@misc{mesure,
    howpublished={\url{https://www-fourier.ujf-grenoble.fr/~edumas/integration.pdf}},
    title={Théorie de la mesure et de l’intégration},
    author={Gallay, Thierry},
    note={Dernier accès: 2024-08-29}
}

@misc{proba,
    title={\url{Intégration, Probabilitées et Processus Aléatoires}},
    howpublished={\url{https://www.imo.universite-paris-saclay.fr/~jean-francois.le-gall/IPPA2.pdf}},
    author={Le Gall, Jean-François},
    note={Dernier accès: 2024-08-29}
}

#Optimisation
@BOOK{ciarlet,
  title    = "Introduction {\`a} l'an{\'a}lyse num{\'e}rique matricielle et
              {\`a} l'optimisation: cours et exercices corrig{\'e}s",
  author   = "Ciarlet, Philippe G",
  year     =  2006,
  language = "fr"
}



#Machine learning

@BOOK{lecun2019quand,
  title     = "Quand la machine apprend",
  author    = "Le Cun, Yann",
  publisher = "Odile Jacob",
  month     =  oct,
  year      =  2019,
  address   = "Paris, France",
  language  = "fr"
}


@article{zou2016finding,
  title={Finding the best classification threshold in imbalanced classification},
  author={Zou, Quan and Xie, Sifa and Lin, Ziyu and Wu, Meihong and Ju, Ying},
  journal={Big Data Research},
  volume={5},
  pages={2--8},
  year={2016},
  publisher={Elsevier}
}


@article{bottou1991stochastic,
  title={Stochastic gradient learning in neural networks},
  author={Bottou, L{\'e}on and others},
  journal={Proceedings of Neuro-N{\i}mes},
  volume={91},
  number={8},
  pages={12},
  year={1991},
  publisher={Nimes}
}

@incollection{bottou2012stochastic,
  title={Stochastic gradient descent tricks},
  author={Bottou, L{\'e}on},
  booktitle={Neural Networks: Tricks of the Trade: Second Edition},
  pages={421--436},
  year={2012},
  publisher={Springer}
}

@article{amari1993back,
  title={Backpropagation and stochastic gradient descent method},
  author={Amari, Shun-ichi},
  journal={Neurocomputing},
  volume={5},
  number={4-5},
  pages={185--196},
  year={1993},
  publisher={Elsevier}
}

@article{kumari2017machine,
  title={Machine learning: A review on binary classification},
  author={Kumari, Roshan and Srivastava, Saurabh Kr},
  journal={International Journal of Computer Applications},
  volume={160},
  number={7},
  year={2017},
  publisher={Foundation of Computer Science}
}
@article{li2020statistical,
  title={Statistical hypothesis testing versus machine learning binary classification: Distinctions and guidelines},
  author={Li, Jingyi Jessica and Tong, Xin},
  journal={Patterns},
  volume={1},
  number={7},
  year={2020},
  publisher={Elsevier}
}
@article{canbek2022ptopi,
  title={PToPI: A comprehensive review, analysis, and knowledge representation of binary classification performance measures/metrics},
  author={Canbek, G{\"u}rol and Taskaya Temizel, Tugba and Sagiroglu, Seref},
  journal={SN Computer Science},
  volume={4},
  number={1},
  pages={13},
  year={2022},
  publisher={Springer}
}

@misc{insee1982parite,
    howpublished={\url{https://www.insee.fr/fr/statistiques/4768237}},
    title={Les cadres : de plus en plus de femmes},
    author={Forment, Virginie and Vidalenc, Joëlle},
    note={Dernier accès: 2024-08-26}
}

@article{chicco2021matthews,
  title={The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation},
  author={Chicco, Davide and T{\"o}tsch, Niklas and Jurman, Giuseppe},
  journal={BioData mining},
  volume={14},
  pages={1--22},
  year={2021},
  publisher={Springer}
}


    
#Equitée

@misc{servicepubdiscrimination,
    title="Qu'est-ce que la discrimination ?",
    howpublished={https://www.service-public.fr/particuliers/vosdroits/F38175},
    note={Dernier accès: 2024-09-13}
}


@BOOK{biddle2006adverse,
  title     = "Adverse impact and test validation",
  author    = "Biddle, Dan",
  publisher = "Gower Publishing",
  edition   =  2,
  month     =  jul,
  year      =  2006,
  address   = "London, England",
  language  = "en"
}


@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
          and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
          and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
          Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}








############################################""
#Enjeux

#Explicabilite
@article{yuan2022explainability,
  title={Explainability in graph neural networks: A taxonomic survey},
  author={Yuan, Hao and Yu, Haiyang and Gui, Shurui and Ji, Shuiwang},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={45},
  number={5},
  pages={5782--5799},
  year={2022},
  publisher={IEEE}
}
@article{du2019techniques,
  title={Techniques for interpretable machine learning},
  author={Du, Mengnan and Liu, Ninghao and Hu, Xia},
  journal={Communications of the ACM},
  volume={63},
  number={1},
  pages={68--77},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{rai2020explainable,
  title={Explainable AI: From black box to glass box},
  author={Rai, Arun},
  journal={Journal of the Academy of Marketing Science},
  volume={48},
  pages={137--141},
  year={2020},
  publisher={Springer}
}

@article{ucoglu2020current,
  title={Current machine learning applications in accounting and auditing},
  author={Ucoglu, Derya},
  journal={PressAcademia Procedia},
  volume={12},
  number={1},
  pages={1--7},
  year={2020},
  publisher={Pressacademia}
}

@article{choi2020identifying,
  title={Identifying machine learning techniques for classification of target advertising},
  author={Choi, Jin-A and Lim, Kiho},
  journal={ICT Express},
  volume={6},
  number={3},
  pages={175--180},
  year={2020},
  publisher={Elsevier}
}


#Securité
#Backdoor
@article{gao2020backdoor,
  title={Backdoor attacks and countermeasures on deep learning: A comprehensive review},
  author={Gao, Yansong and Doan, Bao Gia and Zhang, Zhi and Ma, Siqi and Zhang, Jiliang and Fu, Anmin and Nepal, Surya and Kim, Hyoungshick},
  journal={arXiv preprint arXiv:2007.10760},
  year={2020}
}

@inproceedings{doan2021lira,
  title={Lira: Learnable, imperceptible and robust backdoor attacks},
  author={Doan, Khoa and Lao, Yingjie and Zhao, Weijie and Li, Ping},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={11966--11976},
  year={2021}
}

#Confidentialité 
@misc{discordgpt,
    title={In-Channel Conversation Summaries},
    author={\url{https://support.discord.com/hc/en-us/profiles/2921470028-Buffy}},
    howpublished={\url{https://support.discord.com/hc/en-us/articles/12926016807575-In-Channel-Conversation-Summaries}},
    note={Dernier accès: 2024-08-26}
}


#Fairness
@article{dressel2018accuracy,
  title={The accuracy, fairness, and limits of predicting recidivism},
  author={Dressel, Julia and Farid, Hany},
  journal={Science advances},
  volume={4},
  number={1},
  pages={eaao5580},
  year={2018},
  publisher={American Association for the Advancement of Science}
}



#####################################################""
#Echelle institutionelle 

#Justice prédictive
@article{brayne2015predictive,
  title={Predictive policing},
  author={Brayne, Sarah and Rosenblat, Alex and Boyd, Danah},
  journal={Data \& Civil Rights: A New Era Of Policing And Justice},
  pages={2015--1027},
  year={2015}
}

@misc{soundthinking,
    howpublished={\url{https://www.soundthinking.com/}},
    title={Soundthinking},
    note={Dernier accès: 2024-08-16}
}


@article{
zhiyuan2020limits,
author = {Zhiyuan “Jerry” Lin  and Jongbin Jung  and Sharad Goel  and Jennifer Skeem },
title = {The limits of human predictions of recidivism},
journal = {Science Advances},
volume = {6},
number = {7},
pages = {eaaz0652},
year = {2020},
doi = {10.1126/sciadv.aaz0652},
URL = {https://www.science.org/doi/abs/10.1126/sciadv.aaz0652},
eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.aaz0652},
abstract = {Statistical algorithms can outperform human predictions of recidivism. Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid’s experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other datasets we examined. The performance gap between humans and algorithms was particularly pronounced when, in a departure from the original study, participants were not provided with immediate feedback on the accuracy of their responses. Algorithms also outperformed humans when the information provided for predictions included an enriched (versus restricted) set of risk factors. These results suggest that algorithms can outperform human predictions of recidivism in ecologically valid settings.}}

@misc{equivant,
    howpublished={\url{https://www.equivant.com/}},
    title={Equivant},
    note={Dernier accès: 2024-07-24}
}
@article{dildar2021skin,
  title={Skin cancer detection: a review using deep learning techniques},
  author={Dildar, Mehwish and Akram, Shumaila and Irfan, Muhammad and Khan, Hikmat Ullah and Ramzan, Muhammad and Mahmood, Abdur Rehman and Alsaiari, Soliman Ayed and Saeed, Abdul Hakeem M and Alraddadi, Mohammed Olaythah and Mahnashi, Mater Hussen},
  journal={International journal of environmental research and public health},
  volume={18},
  number={10},
  pages={5479},
  year={2021},
  publisher={MDPI}
}


####################################
#Médecine
@article{gulshan2016development,
  title={Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs},
  author={Gulshan, Varun and Peng, Lily and Coram, Marc and Stumpe, Martin C and Wu, Derek and Narayanaswamy, Arunachalam and Venugopalan, Subhashini and Widner, Kasumi and Madams, Tom and Cuadros, Jorge and others},
  journal={jama},
  volume={316},
  number={22},
  pages={2402--2410},
  year={2016},
  publisher={American Medical Association}
}

@article{quinn2022three,
  title={The three ghosts of medical AI: Can the black-box present deliver?},
  author={Quinn, Thomas P and Jacobs, Stephan and Senadeera, Manisha and Le, Vuong and Coghlan, Simon},
  journal={Artificial intelligence in medicine},
  volume={124},
  pages={102158},
  year={2022},
  publisher={Elsevier}
}


##################################
#Recrutement
@misc{fortune500,
    title={Fortune 500},
    howpublished={\url{https://fortune.com/ranking/global500/}},
    note={Dernier accès: 2024-07-24}
}

@article{ore2022opportunities,
  title={Opportunities and risks of artificial intelligence in recruitment and selection},
  author={Ore, Olajide and Sposato, Martin},
  journal={International Journal of Organizational Analysis},
  volume={30},
  number={6},
  pages={1771--1782},
  year={2022},
  publisher={Emerald Publishing Limited}
}

@inproceedings{al2021role,
  title={The role of artificial intelligence in recruitment process decision-making},
  author={Al-Alawi, Adel Ismail and Naureen, Misbah and AlAlawi, Ebtesam Ismaeel and Al-Hadad, Ahmed Abdulla Naser},
  booktitle={2021 International Conference on Decision Aid Sciences and Application (DASA)},
  pages={197--203},
  year={2021},
  organization={IEEE}
}

@misc{segal2021fairnesseyesdatacertifying,
      title={Fairness in the Eyes of the Data: Certifying Machine-Learning Models}, 
      author={Shahar Segal and Yossi Adi and Benny Pinkas and Carsten Baum and Chaya Ganesh and Joseph Keshet},
      year={2021},
      eprint={2009.01534},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2009.01534}, 
}
@article{Hardt2016equality,
  author    = {Moritz Hardt and
               Eric Price and
               Nathan Srebro},
  title     = {Equality of Opportunity in Supervised Learning},
  journal   = {CoRR},
  volume    = {abs/1610.02413},
  year      = {2016},
  url       = {http://arxiv.org/abs/1610.02413},
  eprinttype = {arXiv},
  eprint    = {1610.02413},
  timestamp = {Tue, 26 Apr 2022 09:17:17 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HardtPS16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{Dwork2011fairness,
  doi = {10.48550/ARXIV.1104.3913},
  
  url = {https://arxiv.org/abs/1104.3913},
  
  author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Rich},
  
  keywords = {Computational Complexity (cs.CC), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Fairness Through Awareness},
  
  publisher = {arXiv},
  
  year = {2011},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}



@inproceedings{10.1145/3278721.3278779,
author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
title = {Mitigating Unwanted Biases with Adversarial Learning},
year = {2018},
isbn = {9781450360128},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3278721.3278779},
doi = {10.1145/3278721.3278779},
abstract = {Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. The objective is to maximize the predictor's ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of giradient-based learning models, including both regression and classification tasks.},
booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
pages = {335–340},
numpages = {6},
keywords = {multi-task learning, debiasing, adversarial learning, unbiasing},
location = {New Orleans, LA, USA},
series = {AIES '18}
}








#####################################################""
#Echelle individuelle 

@misc{applewatch,
    title={WatchOS 11 brings powerful health and fitness insights, and even more personalization and connectivity },
    howpublished={\url{https://www.apple.com/newsroom/2024/06/watchos-11-brings-powerful-health-and-fitness-insights/}},
    note={Dernier accès: 2024-07-24}
}

@inproceedings{barthelemy:hal-01837361,
  TITLE = {{Pl@ntNet, une plate-forme innovante d'agr{\'e}gation et partage d'observations botaniques}},
  AUTHOR = {Barth{\'e}l{\'e}my, Daniel and Boujemaa, Nozha and Molino, Jean-Fran{\c c}ois and Joly, Alexis and Go{\"e}au, Herv{\'e} and Baki{\'c}, Vera and Selmi, Souheil and Champ, Julien and Carre, Jennifer and Chouet, Mathias and Perronnet, Aur{\'e}lien and Vignau, Christelle and Dufour-Kowalski, Samuel and Affouard, Antoine and Barbe, Julien and Bonnet, Pierre},
  URL = {https://hal.science/hal-01837361},
  BOOKTITLE = {{International Conference ‘Botanists of the Twenty-first Century'}},
  ADDRESS = {Paris, France},
  ORGANIZATION = {{UNESCO}},
  HAL_LOCAL_REFERENCE = {DEVMP},
  EDITOR = {No{\"e}line R. Rakotoarisoa and Stephen Blackmore and Bernard Riera},
  PAGES = {191-197},
  YEAR = {2014},
  MONTH = Sep,
  KEYWORDS = {Pl@ntNet ; Botany ; Plateforme participative ; Observations botaniques},
  PDF = {https://hal.science/hal-01837361/file/DB_etal_plantnet_plateforme_2016_1.pdf},
  HAL_ID = {hal-01837361},
  HAL_VERSION = {v1},
}

@misc{plantnet,
    title={Pl@ntNet},
    howpublished={\url{https://identify.plantnet.org/}},
    note={Dernier accès: 2024-07-24}
}


@article{dunn2018wearables,
  title={Wearables and the medical revolution},
  author={Dunn, Jessilyn and Runge, Ryan and Snyder, Michael},
  journal={Personalized medicine},
  volume={15},
  number={5},
  pages={429--448},
  year={2018},
  publisher={Taylor \& Francis}
}

########################################"
#Intéret pour l'IA
#Google trend 
@misc{gtrend,
    title={Google trend Intelligence Artificielle},
    howpublished={\url{https://trends.google.com/trends/explore?date=all&geo=FR&q=intelligence%20artificielle&hl=en-US}},
    note={Dernier accès: 2024-07-24}
}

####################################""
#Stratégie AI de la France
@article{touvron2023llama,
  title={Llama 2: Open foundation and fine-tuned chat models},
  author={Touvron, Hugo and Martin, Louis and Stone, Kevin and Albert, Peter and Almahairi, Amjad and Babaei, Yasmine and Bashlykov, Nikolay and Batra, Soumya and Bhargava, Prajjwal and Bhosale, Shruti and others},
  journal={arXiv preprint arXiv:2307.09288},
  year={2023}
}
@misc{g5k,
    title={Grid5000},
    howpublished={\url{www.grid5000.fr}},
    note={Dernier accès: 2024-09-18}
}
@misc{jeanzay,
    title={Jean Zay, le supercalculateur le plus puissant de France pour la recherche}
    howpublished={\url{https://www.cnrs.fr/fr/presse/jean-zay-le-supercalculateur-le-plus-puissant-de-france-pour-la-recherche}},
    note={Dernier accès: 2024-09-18}
}
@misc{2030phase,
    title={La stratégie nationale pour l'intelligence artificielle},
    howpublished={\url{https://www.entreprises.gouv.fr/fr/numerique/enjeux/la-strategie-nationale-pour-l-ia}},
    note={Dernier accès: 2024-09-18}
}
@misc{coordinateur,
    title={France 2030 | Nomination du coordinateur national pour l’intelligence artificielle},
    howpublished={\url{https://www.info.gouv.fr/actualite/france-2030-nomination-du-coordinateur-national-pour-l-intelligence-artificielle}},
    note={Dernier accès: 2024-09-17}
}

@misc{loinumerique,
    title={LOI n° 2016-1321 du 7 octobre 2016 pour une République numérique},
    howpublished={\url{https://www.legifrance.gouv.fr/jorf/id/JORFSCTA000033202935}}
    note={Dernier accès: 2024-09-17}
}
@misc{kaggle,
    title={Kaggle},
    howpublished={\url{kaggle.com}},
    note={Dernier accès: 2024-09-17}
}
@misc{2030sante,
    title={Data Challenges en santé},
    howpublished={\url{https://www.bpifrance.fr/nos-appels-a-projets-concours/appel-a-projets-data-challenges-en-sante}},
    note={Dernier accès: 2024-09-17}
}

@misc{iabooster,
    title={IA Booster},
    howpublished={\url{https://www.bpifrance.fr/catalogue-offres/ia-booster-france-2030}},
    note={Dernier accès: 2024-09-17}
}
@misc{2030generatif,
    title={Accélérer l’usage de l’intelligence artificielle générative dans l’économie},
    howpublished={\url{https://www.bpifrance.fr/nos-appels-a-projets-concours/appel-a-projets-accelerer-lusage-de-lintelligence-artificielle-generative-dans-leconomie}},
    note={Dernier accès: 2024-09-17}
}

@misc{france2030,
    title={France 2030},
    howpublished={\url{https://www.info.gouv.fr/grand-dossier/france-2030}},
    note={Dernier accès: 2024-07-24}
}

@misc{stratfr,
    title={La stratégie nationale pour l'intelligence artificielle},
    howpublished={\url{https://www.entreprises.gouv.fr/fr/numerique/enjeux/la-strategie-nationale-pour-l-ia}},
    note={Dernier accès: 2024-07-24}
}

@book{villani2018donner,
  TITLE = {{Donner un sens {\`a} l'intelligence artificielle}},
  AUTHOR = {Villani, C{\'e}dric and Schoenauer, Marc and Bonnet, Yann and Berthet, Charly and Cornut, Anne-Charlotte and Levin, Fran{\c c}ois and Rondepierre, Bertrand},
  URL = {https://inria.hal.science/hal-01967551},
  PUBLISHER = {{Mission Villani sur l'intelligence artificielle}},
  YEAR = {2018},
  MONTH = Mar,
  PDF = {https://inria.hal.science/hal-01967551/file/9782111457089_Rapport_Villani_accessible.pdf},
  HAL_ID = {hal-01967551},
  HAL_VERSION = {v1},
}

%%%%%%%%%%%CLIMATE CHANGE BACKGROUND
@article{barnes2019viewing,
  title={Viewing forced climate patterns through an AI lens},
  author={Barnes, Elizabeth A and Hurrell, James W and Ebert-Uphoff, Imme and Anderson, Chuck and Anderson, David},
  journal={Geophysical Research Letters},
  volume={46},
  number={22},
  pages={13389--13398},
  year={2019},
  publisher={Wiley Online Library}
}

@article{slater2023hybrid,
  title={Hybrid forecasting: blending climate predictions with AI models},
  author={Slater, Louise J and Arnal, Louise and Boucher, Marie-Am{\'e}lie and Chang, Annie Y-Y and Moulds, Simon and Murphy, Conor and Nearing, Grey and Shalev, Guy and Shen, Chaopeng and Speight, Linda and others},
  journal={Hydrology and earth system sciences},
  volume={27},
  number={9},
  pages={1865--1889},
  year={2023},
  publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

%%%%%%%%%%%%ENERGY BACKGROUND
@article{jin2020energy,
  title={Energy and AI},
  author={Jin, Donghan and Ocone, Raffaella and Jiao, Kui and Xuan, Jin},
  journal={Energy and AI},
  volume={1},
  pages={100002},
  year={2020},
  publisher={Elsevier}
}

@article{kumar2020distributed,
  title={Distributed energy resources and the application of AI, IoT, and blockchain in smart grids},
  author={Kumar, Nallapaneni Manoj and Chand, Aneesh A and Malvoni, Maria and Prasad, Kushal A and Mamun, Kabir A and Islam, FR and Chopra, Shauhrat S},
  journal={Energies},
  volume={13},
  number={21},
  pages={5739},
  year={2020},
  publisher={MDPI}
}

@article{kumari2020blockchain,
  title={Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions},
  author={Kumari, Aparna and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj},
  journal={Journal of Parallel and Distributed Computing},
  volume={143},
  pages={148--166},
  year={2020},
  publisher={Elsevier}
}

@article{ngarambe2020use,
  title={The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: Energy implications of AI-based thermal comfort controls},
  author={Ngarambe, Jack and Yun, Geun Young and Santamouris, Mat},
  journal={Energy and Buildings},
  volume={211},
  pages={109807},
  year={2020},
  publisher={Elsevier}
}


%%%%%OPEN AI 

@misc{openaibfm,
    title={OpenAI, cette société qui révolutionne l'intelligence artificielle},
    howpublished={\url{https://www.bfmtv.com/tech/intelligence-artificielle/open-ai-cette-societe-qui-revolutionne-l-intelligence-artificielle_DN-202311200564.html}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaiinter,
    title={Intelligence artificielle : pourquoi Sam Altman, créateur de ChatGPT, a été débarqué d'OpenAI},
    howpublished={\url{https://www.radiofrance.fr/franceinter/ce-que-l-on-sait-du-renvoi-de-sam-altman-patron-d-openai-et-createur-de-chatgpt-5672369}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaint,
    title={OpenAI Says It Has Begun Training a New Flagship A.I. Model},
    howpublished={\url{https://www.nytimes.com/2024/05/28/technology/openai-gpt4-new-model.html}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaibig,
    title={ChatGPT sets record for fastest-growing user base - analyst note},
    howpublished={\url{https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/}},
    note={Dernier accès: 2024-07-24}
}

@misc{gptjournal,
    title={ChatGPT : le quotidien Le Monde signe un partenariat avec OpenAI, une première en France},
    howpublished={\url{https://www.radiofrance.fr/franceinter/podcasts/l-info-de-france-inter/les-doc-france-inter-du-jeudi-14-mars-3-7619379}},
    note={Dernier accès: 2024-07-24}
}

##################################
#Chine surveillance de la population

@article{beraja2023ai,
  title={AI-tocracy},
  author={Beraja, Martin and Kao, Andrew and Yang, David Y and Yuchtman, Noam},
  journal={The Quarterly Journal of Economics},
  volume={138},
  number={3},
  pages={1349--1402},
  year={2023},
  publisher={Oxford University Press}
}

################################
#Définition
@article{baum2017survey,
  title={A survey of artificial general intelligence projects for ethics, risk, and policy},
  author={Baum, Seth},
  journal={Global Catastrophic Risk Institute Working Paper},
  pages={17--1},
  year={2017}
}

@inproceedings{kuppa2021towards,
  title={Towards improving privacy of synthetic datasets},
  author={Kuppa, Aditya and Aouad, Lamine and Le-Khac, Nhien-An},
  booktitle={Annual Privacy Forum},
  pages={106--119},
  year={2021},
  organization={Springer}
}
@inproceedings{arpit2017closer,
  title={A closer look at memorization in deep networks},
  author={Arpit, Devansh and Jastrzebski, Stanislaw and Ballas, Nicolas and Krueger, David and Bengio, Emmanuel and Kanwal, Maxinder S and Maharaj, Tegan and Fischer, Asja and Courville, Aaron and Bengio, Yoshua and others},
  booktitle={International conference on machine learning},
  pages={233--242},
  year={2017},
  organization={PMLR}
}
@inproceedings{feldman2020does,
  title={Does learning require memorization? a short tale about a long tail},
  author={Feldman, Vitaly},
  booktitle={Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing},
  pages={954--959},
  year={2020}
}

@book{theetete,
    title={Théétète},
    author={Platon},
    year={300 av. JC}
}
@book{caverne,
    title={La République},
    author={Platon},
    year={300 av. JC}
}
@misc{dartmouth,
    title={Dartmouth summer research project on artificiale intelligence},
    howpublished={\url{https://raysolomonoff.com/dartmouth/boxa/dart564props.pdf}},
    author={
        McCarthy, John and Minsky, Marvin and Rochester Nathaniel and Shannon, Claude
    },
    note={Dernier accès: 2024-08-05}
}

@misc{banIA,
    title={En 2024, bannissons les termes "intelligence artificielle"},
    howpublished={\url{https://www.radiofrance.fr/franceculture/podcasts/le-biais-d-aurelie-jean/le-biais-d-aurelie-jean-chronique-du-mardi-02-janvier-2024-9653995}},
    author={Jean, Aurélie},
    note={Dernier accès: 2024-08-05}
}

@misc{gnuAI,
    title={Words to Avoid (or Use with Care) Because They Are Loaded or Confusing.},
    howpublished={\url{https://www.gnu.org/philosophy/words-to-avoid.html#ArtificialIntelligence}},
    note={Dernier accès: 2024-08-05}
}
@book{dico-int,
    title={Dictionaire de l'Académie francaise, 9° édition},
    note={\url{http://www.dictionnaire-academie.fr/article/A9I1608}, Dernier accès: 2024-08-05}
}
@book{dico-art,
    title={Dictionaire de l'Académie francaise, 9° édition},
    note={\url{http://www.dictionnaire-academie.fr/article/A9A2706},Dernier accès: 2024-08-05}
}
@book{dico-con,
    title={Dictionaire de l'Académie francaise, 9° édition},
    note={\url{https://www.dictionnaire-academie.fr/article/A9C3633},Dernier accès: 2024-08-16}
}
@misc{underscore,
    title={Cette nouvelle IA est bluffante},
    author={Chaîne Youtube Underscore},
    year={2024},
    howpublished={\url{https://www.youtube.com/watch?v=QUr93cD2ZUs}},
    note={Dernier accès: 2024-08-05}
}
@misc{grep,
    title={grep},
    howpublished={\url{https://www.gnu.org/software/grep/manual/grep.html}},
    note={Dernier accès: 2024-08-05}
}
@misc{ocrad,
    title={Ocrad},
    howpublished={\url{https://www.gnu.org/software/ocrad/ocrad.html}},
    note={Dernier accès: 2024-08-05}
}

@misc{aiact,
    howpublished={\url{https://eur-lex.europa.eu/eli/reg/2024/1689/oj}},

    note={Dernier accès: 2024-09-02},
    title={Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance)}
}



#########################################
#Philosophie

@misc{siri,
    title={Siri},
    author={Apple},
    howpublished={\url{https://www.apple.com/siri/}},
    note={Dernier accès: 2024-08-26}
}

@misc{discord,
    title={Messagerie Discord},
    author={Discord},
    howpublished={\url{https://discord.com/}},
    note={Dernier accès: 2024-08-26}
}

@misc{googleai,
    title={Google assistant},
    author={Google},
    howpublished={\url{https://assistant.google.com/}},
    note={Dernier accès: 2024-08-26}
}

@misc{aaigpt,
    title={Apple inteligence ChatGPT},
    author={Apple},
    howpublished={\url{https://www.apple.com/newsroom/2024/06/introducing-apple-intelligence-for-iphone-ipad-and-mac/}},
    note={Dernier accès: 2024-08-26}
}



@BOOK{Freud2010-qq,
  title     = "Le moi et le {\c c}a",
  author    = "Freud, Sigmund",
  publisher = "Payot",
  year      =  2010,
  language  = "fr",
  note={Das Ich und das Es, 1923. Traduction Jean Laplanche}
}

@article{waters2014grade,
  title={Grade: Machine learning support for graduate admissions},
  author={Waters, Austin and Miikkulainen, Risto},
  journal={Ai Magazine},
  volume={35},
  number={1},
  pages={64--64},
  year={2014}
}


@book{rousseau1762contrat,
    title={Du contrat social ou Principes du droit politique},
    author={Rousseau, Jean-Jeacques},
    year={1762}
}

@BOOK{Poundstone1993-jr,
  title     = "Prisoner's Dilemma",
  author    = "Poundstone, William",
  publisher = "Anchor Books",
  month     =  jan,
  year      =  1993,
  address   = "New York, NY"
}


@article{wang2023not,
  title={Do-not-answer: A dataset for evaluating safeguards in llms},
  author={Wang, Yuxia and Li, Haonan and Han, Xudong and Nakov, Preslav and Baldwin, Timothy},
  journal={arXiv preprint arXiv:2308.13387},
  year={2023}
}

@article{bergeaud2023teletravail,
  title={T{\'e}l{\'e}travail et productivit{\'e} avant, pendant et apr{\`e}s la pand{\'e}mie de Covid-19/Telework and Productivity Before, During and After the COVID-19 Crisis},
  author={Bergeaud, Antonin and Cette, Gilbert and Drapala, Simon},
  journal={Economie et Statistique},
  volume={539},
  number={1},
  pages={77--93},
  year={2023},
  publisher={Pers{\'e}e-Portail des revues scientifiques en SHS}
}

@misc{metaverse,
    title={What is the metaverse?},
    author={Meta},
    howpublished={\url{https://about.meta.com/what-is-the-metaverse/}},
    note={Dernier accès: 2024-08-22}
}

@misc{applevision,
    title={Apple vision pro},
    howpublished={\url{https://www.apple.com/apple-vision-pro/}},
    author={Apple},
    note={Dernier accès: 2024-08-22}



@article{johnson2017ai,
  title={AI anxiety},
  author={Johnson, Deborah G and Verdicchio, Mario},
  journal={Journal of the Association for Information Science and Technology},
  volume={68},
  number={9},
  pages={2267--2270},
  year={2017},
  publisher={Wiley Online Library}
}

@misc{afi100,
    title={100 YEARS...100 MOVIES},
    author={AMERICAN FILM INSTITUTE},
    howpublished={\url{https://www.afi.com/afis-100-years-100-movies-10th-anniversary-edition/}},
    note={Dernier accès: 2024-08-21}
}


@article{bernays1928manipulating,
author = {Bernays, Edward L.},
title = {Manipulating Public Opinion: The Why and The How},
journal = {American Journal of Sociology},
volume = {33},
number = {6},
pages = {958-971},
year = {1928},
doi = {10.1086/214599},
URL = { 
    
        https://doi.org/10.1086/214599
},
eprint = { 
    
        https://doi.org/10.1086/214599
}
,
    abstract = { Public opinion, narrowly defined, is the thought of a society at a given time toward a given object; broadly conceived, it is the power of the group to sway the larger public in its attitude. Public opinion can be manipulated, but in teaching the public how to ask for what it wants the manipulator is safeguarding the public against his own possible aggressiveness. The method of the experimental psychologist is not as effective in the study of public opinion in the broad sense as is that of introspective psychology. To create and to change public opinion it is necessary to understand human motives, to know what special interests are represented by a given population, and to realize the function and limitations of the physical organs of approach to the public, such as the radio, the platform, the movie, the letter, the newspaper, etc. If the general principles of swaying public opinion are understood, a technique can be developed which, with the correct appraisal of the specific problem and the specific audience, can and has been used effectively in such widely different situations as changing the attitudes of whites toward Negroes in America, changing the buying habits of American women from felt hats to velvet, silk, and straw hats, changing the impression which the American electorate has of its President, introducing new musical instruments, and a variety of others. Group adherence is essential in changing the attitudes of the public. Authoritative and influential groups may become important channels of reaching the larger public. Ideas and situations must be made impressive and dramatic in order to overcome the inertia of established traditions and prejudices. }
}



@article{fearing1947influence,
author = {Franklin Fearing},
title ={Influence of the Movies on Attitudes and Behavior},

journal = {The ANNALS of the American Academy of Political and Social Science},
volume = {254},
number = {1},
pages = {70-79},
year = {1947},
doi = {10.1177/000271624725400112},

URL = { 
        https://doi.org/10.1177/000271624725400112
},
eprint = { 
        https://doi.org/10.1177/000271624725400112
}
}
@misc{roko,
    title={Solutions to the Altruist's burden: the Quantum Billionaire Trick},
    year={2010},
    author={Roko},
    howpublished={\url{https://rationalwiki.org/wiki/Roko%27s_basilisk/Original_post#Solutions_to_the_Altruist.27s_burden:_the_Quantum_Billionaire_Trick}},
    note={Dernier accès: 2024-08-22}
}
@misc{rokowiki,
    title={Roko's basilisk},
    howpublished={\url{https://old-wiki.lesswrong.com/wiki/Roko%27s_basilisk#Roko's_post}},
    note={Dernier accès: 2024-08-22}
}
@misc{slate,
    title={The Most Terrifying Thought Experiment of All Time},
    author={Auerbach, David},
    howpublished={\url{https://slate.com/technology/2014/07/rokos-basilisk-the-most-terrifying-thought-experiment-of-all-time.html}},
    note={Dernier accès: 2024-08-22}
}
@misc{rokomisc,
    title={A few misconceptions surrounding Roko's basilisk},
    author={Bensinger, Rob}
    howpublished={\url{https://www.lesswrong.com/posts/WBJZoeJypcNRmsdHx/a-few-misconceptions-surrounding-roko-s-basilisk}},
    note={Dernier accès: 2024-08-22}
}

@article{Singler_2018, title={Roko’s Basilisk or Pascal’s? Thinking of Singularity Thought Experiments as Implicit Religion}, volume={20},  url={https://journal.equinoxpub.com/IR/article/view/3226},  DOI={10.1558/imre.35900}, abstractNote={In 2010 a thought experiment speculating on the motivations and aims of a potential superintelligent Artificial Intelligence, sometimes known as the ‘Singularity’, caused uproar and anxiety on the forum board where it was initially posted. This paper considers that thought experiment’s debt to older forms of religious argument, the reactions from among the community, and how expectations about the Singularity as a being with agency can be considered to be an example of implicit religion. This is significant as the thought experiment appeared in a field of research, AI, considered by many to be secular due to its technological focus. The communities under discussion also explicitly express their aim of ‘perfecting’ human rationality, and place that ability in opposition to ‘religion’ as a derided object and the aims of ‘Goddists’ in general. This tension between overt atheism and secular communities’ return to religious tropes and narratives is relevant for the wider study of religion in the contemporary era.}, number={3}, journal={Implicit Religion}, author={Singler, Beth}, year={2018}, month={May}, pages={279–297} }

@misc{matrix,
    title={The Matrix},
    author={Wachowski and Silver and Pope},
    year={1999}
}

@misc{her,
    title={Her},
    author={Jonze, Spike},
    year={2013}
}

@misc{johansson,
    title={Scarlett Johansson’s Statement About Her Interactions With Sam Altman},
    howpublished={\url{https://www.nytimes.com/2024/05/20/technology/scarlett-johansson-openai-statement.html}},
    note={Dernier accès: 2024-08-21}
}


@book{bicentenaire,
    title={The Bicentennial Man},
    author={Asimov,Isaac},
    year={1976},
}


@misc{avenger,
    title={Avengers: Age of Ultron},
    author={ Whedon, Joss and Feige, Kevin},
    year={2015},
    note={Based on the comics by Stan Lee and Jack Kirby}
}
@misc{terminator,
    title={The Terminator},
    author={Cameron, James and Hurd, Gale Anne},
    year={1999}
}

@misc{2001odyssey,
    title={2001: A space odyssey},
    author={Kubrick, Stanley and Clarke, Arthur C. },
    year={1968}
}
@misc{futurama,
    title={Futurama},
    author={Groening, Matt},
    year={2003}
}

@misc{wargames,
    title={War games},
    author={Badham, John and Lasker, Lawrence and Parkes, Walter F. and Schneider,Harold},
    year={1983}
}

@book{assimovIrobot,
    title={I, Robot},
    year={1950},
    author={Isaac Asimov}
}

@book{cornu,
    title={Vocabulaire juridique},
    author={Cornu, Gérard},
    year={2014},
    note={Dixième édition}
}

@article{MARAKAS2000719,
title = {A theoretical model of differential social attributions toward computing technology: when the metaphor becomes the model},
journal = {International Journal of Human-Computer Studies},
volume = {52},
number = {4},
pages = {719-750},
year = {2000},
issn = {1071-5819},
doi = {https://doi.org/10.1006/ijhc.1999.0348},
url = {https://www.sciencedirect.com/science/article/pii/S1071581999903488},
author = {GEORGE M. MARAKAS and RICHARD D. JOHNSON and JONATHAN W. PALMER},
keywords = {anthropomorphism, symbolic computing, social acts, laws of control, computer self-efficiency.},
abstract = {This paper explores the use of metaphorical personification (anthropomorphism) as an aid to describing and understanding the complexities of computing technologies. This common and seemingly intuitive practice (it “reads”, “writes”, “thinks”, “is friendly”, “catches and transmits viruses”, etc.) has become the standard by which we formulate our daily communications, and often our formal training mechanisms, with regard to the technology. Both anecdotal and empirical sources have reported numerous scenarios in which computers have played a noticeably social role, thus being positioned more as a social actor than as a machine or “neutral tool.” In these accounts, human behavior has ranged from making social reference to the device (“It's really much smarter than me,”), to more overt social interactions including conversational interplay and display of common human emotions in response to an interaction. Drawing from behavioral psychology and attribution theory, a theoretical model of the phenomenon is offered from which several propositions are advanced regarding the nature of the behavior, positive and negative implications associated with extended use of this metaphor, and recommendations for research into this ubiquitous social phenomena. … I have encountered these situations before, and in every case they were the result of human error. -HAL 9000 from Arthur C. Clarke's 2001: A Space Odyssey}
}


@article{searle1980minds,
  title={Minds, brains, and programs},
  author={Searle, John R},
  journal={Behavioral and brain sciences},
  volume={3},
  number={3},
  pages={417--424},
  year={1980},
  publisher={Cambridge University Press}
}

@misc{oms,
    title={Rapport de l'Organisation Mondiale de la Santé},
    howpublished={\url{https://www.who.int/fr/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use}},
    author={OMS},
    year={2021}
}


###############################################
#Synthetic 
@misc{carlini2022membershipinferenceattacksprinciples,
      title={Membership Inference Attacks From First Principles}, 
      author={Nicholas Carlini and Steve Chien and Milad Nasr and Shuang Song and Andreas Terzis and Florian Tramer},
      year={2022},
      eprint={2112.03570},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2112.03570}, 
}

@article{brayne2015predictive,
  title={Predictive policing},
  author={Brayne, Sarah and Rosenblat, Alex and Boyd, Danah},
  journal={Data \& Civil Rights: A New Era Of Policing And Justice},
  pages={2015--1027},
  year={2015}
}

@inproceedings{barthelemy:hal-01837361,
  TITLE = {{Pl@ntNet, une plate-forme innovante d'agr{\'e}gation et partage d'observations botaniques}},
  AUTHOR = {Barth{\'e}l{\'e}my, Daniel and Boujemaa, Nozha and Molino, Jean-Fran{\c c}ois and Joly, Alexis and Go{\"e}au, Herv{\'e} and Baki{\'c}, Vera and Selmi, Souheil and Champ, Julien and Carre, Jennifer and Chouet, Mathias and Perronnet, Aur{\'e}lien and Vignau, Christelle and Dufour-Kowalski, Samuel and Affouard, Antoine and Barbe, Julien and Bonnet, Pierre},
  URL = {https://hal.science/hal-01837361},
  BOOKTITLE = {{International Conference ‘Botanists of the Twenty-first Century'}},
  ADDRESS = {Paris, France},
  ORGANIZATION = {{UNESCO}},
  HAL_LOCAL_REFERENCE = {DEVMP},
  EDITOR = {No{\"e}line R. Rakotoarisoa and Stephen Blackmore and Bernard Riera},
  PAGES = {191-197},
  YEAR = {2014},
  MONTH = Sep,
  KEYWORDS = {Pl@ntNet ; Botany ; Plateforme participative ; Observations botaniques},
  PDF = {https://hal.science/hal-01837361/file/DB_etal_plantnet_plateforme_2016_1.pdf},
  HAL_ID = {hal-01837361},
  HAL_VERSION = {v1},
}

@misc{plantnet,
    title={Pl@ntNet},
    howpublished={\url{https://identify.plantnet.org/}},
    note={Dernier accès: 2024-07-24}
}


@article{dunn2018wearables,
  title={Wearables and the medical revolution},
  author={Dunn, Jessilyn and Runge, Ryan and Snyder, Michael},
  journal={Personalized medicine},
  volume={15},
  number={5},
  pages={429--448},
  year={2018},
  publisher={Taylor \& Francis}
}

@misc{gtrend,
    title={Google trend Intelligence Artificielle},
    howpublished={\url{https://trends.google.com/trends/explore?date=all&geo=FR&q=intelligence%20artificielle&hl=en-US}},
    note={Dernier accès: 2024-07-24}
}

@misc{france2030,
    title={France 2030},
    howpublished={\url{https://www.info.gouv.fr/grand-dossier/france-2030}},
    note={Dernier accès: 2024-07-24}
}

@misc{stratfr,
    title={La stratégie nationale pour l'intelligence artificielle},
    howpublished={\url{https://www.entreprises.gouv.fr/fr/numerique/enjeux/la-strategie-nationale-pour-l-ia}},
    note={Dernier accès: 2024-07-24}
}

@misc{applewatch,
    title={WatchOS 11 brings powerful health and fitness insights, and even more personalization and connectivity },
    howpublished={\url{https://www.apple.com/newsroom/2024/06/watchos-11-brings-powerful-health-and-fitness-insights/}},
    note={Dernier accès: 2024-07-24}
}

%%%%%%%%%%%CLIMATE CHANGE BACKGROUND
@article{barnes2019viewing,
  title={Viewing forced climate patterns through an AI lens},
  author={Barnes, Elizabeth A and Hurrell, James W and Ebert-Uphoff, Imme and Anderson, Chuck and Anderson, David},
  journal={Geophysical Research Letters},
  volume={46},
  number={22},
  pages={13389--13398},
  year={2019},
  publisher={Wiley Online Library}
}

@article{slater2023hybrid,
  title={Hybrid forecasting: blending climate predictions with AI models},
  author={Slater, Louise J and Arnal, Louise and Boucher, Marie-Am{\'e}lie and Chang, Annie Y-Y and Moulds, Simon and Murphy, Conor and Nearing, Grey and Shalev, Guy and Shen, Chaopeng and Speight, Linda and others},
  journal={Hydrology and earth system sciences},
  volume={27},
  number={9},
  pages={1865--1889},
  year={2023},
  publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

%%%%%%%%%%%%ENERGY BACKGROUND
@article{jin2020energy,
  title={Energy and AI},
  author={Jin, Donghan and Ocone, Raffaella and Jiao, Kui and Xuan, Jin},
  journal={Energy and AI},
  volume={1},
  pages={100002},
  year={2020},
  publisher={Elsevier}
}

@article{kumar2020distributed,
  title={Distributed energy resources and the application of AI, IoT, and blockchain in smart grids},
  author={Kumar, Nallapaneni Manoj and Chand, Aneesh A and Malvoni, Maria and Prasad, Kushal A and Mamun, Kabir A and Islam, FR and Chopra, Shauhrat S},
  journal={Energies},
  volume={13},
  number={21},
  pages={5739},
  year={2020},
  publisher={MDPI}
}

@article{kumari2020blockchain,
  title={Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions},
  author={Kumari, Aparna and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj},
  journal={Journal of Parallel and Distributed Computing},
  volume={143},
  pages={148--166},
  year={2020},
  publisher={Elsevier}
}

@article{ngarambe2020use,
  title={The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: Energy implications of AI-based thermal comfort controls},
  author={Ngarambe, Jack and Yun, Geun Young and Santamouris, Mat},
  journal={Energy and Buildings},
  volume={211},
  pages={109807},
  year={2020},
  publisher={Elsevier}
}


%%%%%OPEN AI 

@misc{openaibfm,
    title={OpenAI, cette société qui révolutionne l'intelligence artificielle},
    howpublished={\url{https://www.bfmtv.com/tech/intelligence-artificielle/open-ai-cette-societe-qui-revolutionne-l-intelligence-artificielle_DN-202311200564.html}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaiinter,
    title={Intelligence artificielle : pourquoi Sam Altman, créateur de ChatGPT, a été débarqué d'OpenAI},
    howpublished={\url{https://www.radiofrance.fr/franceinter/ce-que-l-on-sait-du-renvoi-de-sam-altman-patron-d-openai-et-createur-de-chatgpt-5672369}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaint,
    title={OpenAI Says It Has Begun Training a New Flagship A.I. Model},
    howpublished={\url{https://www.nytimes.com/2024/05/28/technology/openai-gpt4-new-model.html}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaibg,
    title={ChatGPT sets record for fastest-growing user base - analyst note},
    howpublished={\url{https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/}},
    note={Dernier accès: 2024-07-24}
}

@misc{gptjournal,
    title={ChatGPT : le quotidien Le Monde signe un partenariat avec OpenAI, une première en France},
    howpublished={\url{https://www.radiofrance.fr/franceinter/podcasts/l-info-de-france-inter/les-doc-france-inter-du-jeudi-14-mars-3-7619379}},
    note={Dernier accès: 2024-07-24}
}

@article{beraja2023ai,
  title={AI-tocracy},
  author={Beraja, Martin and Kao, Andrew and Yang, David Y and Yuchtman, Noam},
  journal={The Quarterly Journal of Economics},
  volume={138},
  number={3},
  pages={1349--1402},
  year={2023},
  publisher={Oxford University Press}
}


@article{EO,
  author    = {Moritz Hardt and
               Eric Price and
               Nathan Srebro},
  title     = {Equality of Opportunity in Supervised Learning},
  journal   = {CoRR},
  volume    = {abs/1610.02413},
  year      = {2016},
  url       = {http://arxiv.org/abs/1610.02413},
  eprinttype = {arXiv},
  eprint    = {1610.02413},
  timestamp = {Tue, 26 Apr 2022 09:17:17 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HardtPS16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{hawkins2004problem,
  title={The problem of overfitting},
  author={Hawkins, Douglas M},
  journal={Journal of chemical information and computer sciences},
  volume={44},
  number={1},
  pages={1--12},
  year={2004},
  publisher={ACS Publications}
}


@misc{vgg16,
      title={Very Deep Convolutional Networks for Large-Scale Image Recognition}, 
      author={Karen Simonyan and Andrew Zisserman},
      year={2015},
      eprint={1409.1556},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1409.1556}, 
}
@misc{CGAN,
      title={Conditional Generative Adversarial Nets}, 
      author={Mehdi Mirza and Simon Osindero},
      year={2014},
      eprint={1411.1784},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1411.1784}, 
}
@ARTICLE{cnn,

  author={Rawat, Waseem and Wang, Zenghui},

  journal={Neural Computation}, 

  title={Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review}, 

  year={2017},

  volume={29},

  number={9},

  pages={2352-2449},

  keywords={},

  doi={10.1162/neco_a_00990}}

@misc{dcgan,
      title={Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks}, 
      author={Alec Radford and Luke Metz and Soumith Chintala},
      year={2016},
      eprint={1511.06434},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1511.06434}
}
@inproceedings{gan,
author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
title = {Generative adversarial nets},
year = {2014},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
booktitle = {Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2},
pages = {2672–2680},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS'14}
}
@misc{ctgan,
      title={Modeling Tabular data using Conditional GAN}, 
      author={Lei Xu and Maria Skoularidou and Alfredo Cuesta-Infante and Kalyan Veeramachaneni},
      year={2019},
      eprint={1907.00503},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1907.00503}, 
}
@article{bellovin2019privacy,
  title={Privacy and synthetic datasets},
  author={Bellovin, Steven M and Dutta, Preetam K and Reitinger, Nathan},
  journal={Stan. Tech. L. Rev.},
  volume={22},
  pages={1},
  year={2019},
  publisher={HeinOnline}
}

@inproceedings{ping2017datasynthesizer,
  title={Datasynthesizer: Privacy-preserving synthetic datasets},
  author={Ping, Haoyue and Stoyanovich, Julia and Howe, Bill},
  booktitle={Proceedings of the 29th International Conference on Scientific and Statistical Database Management},
  pages={1--5},
  year={2017}
}

@inproceedings{kuppa2021towards,
  title={Towards improving privacy of synthetic datasets},
  author={Kuppa, Aditya and Aouad, Lamine and Le-Khac, Nhien-An},
  booktitle={Annual Privacy Forum},
  pages={106--119},
  year={2021},
  organization={Springer}
}

@article{tai2023user,
  title={User-Driven Synthetic Dataset Generation with Quantifiable Differential Privacy},
  author={Tai, Bo-Chen and Tsou, Yao-Tung and Li, Szu-Chuang and Huang, Yennun and Tsai, Pei-Yuan and Tsai, Yu-Cheng},
  journal={IEEE Transactions on Services Computing},
  year={2023},
  publisher={IEEE}
}
@article{stadler2020synthetic,
  title={Synthetic data-A privacy mirage},
  author={Stadler, Theresa and Oprisanu, Bristena and Troncoso, Carmela},
  journal={arXiv preprint arXiv:2011.07018},
  year={2020},
  publisher={Nov}
}

@inproceedings{jordon2021hide,
  title={Hide-and-seek privacy challenge: Synthetic data generation vs. patient re-identification},
  author={Jordon, James and Jarrett, Daniel and Saveliev, Evgeny and Yoon, Jinsung and Elbers, Paul and Thoral, Patrick and Ercole, Ari and Zhang, Cheng and Belgrave, Danielle and van der Schaar, Mihaela},
  booktitle={NeurIPS 2020 Competition and Demonstration Track},
  pages={206--215},
  year={2021},
  organization={PMLR}
}

@inproceedings{abadi2016deep,
  title={Deep learning with differential privacy},
  author={Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li},
  booktitle={Proceedings of the 2016 ACM SIGSAC conference on computer and communications security},
  pages={308--318},
  year={2016}
}

@inproceedings{shokri2017membership,
  title={Membership inference attacks against machine learning models},
  author={Shokri, Reza and Stronati, Marco and Song, Congzheng and Shmatikov, Vitaly},
  booktitle={2017 IEEE symposium on security and privacy (SP)},
  pages={3--18},
  year={2017},
  organization={IEEE}
}

@article{ding2021retiring,
  title={Retiring Adult: New Datasets for Fair Machine Learning},
  author={Ding, Frances and Hardt, Moritz and Miller, John and Schmidt, Ludwig},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

@inproceedings{zhifei2017cvpr,
  title={Age Progression/Regression by Conditional Adversarial Autoencoder},
  author={Zhang, Zhifei and Song, Yang and Qi, Hairong},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  organization={IEEE}
}

@misc{carlini2022membership,
      title={Membership Inference Attacks From First Principles}, 
      author={Nicholas Carlini and Steve Chien and Milad Nasr and Shuang Song and Andreas Terzis and Florian Tramer},
      year={2022},
      eprint={2112.03570},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

@inproceedings{salem2023sok,
  title={SoK: Let the privacy games begin! A unified treatment of data inference privacy in machine learning},
  author={Salem, Ahmed and Cherubin, Giovanni and Evans, David and K{\"o}pf, Boris and Paverd, Andrew and Suri, Anshuman and Tople, Shruti and Zanella-B{\'e}guelin, Santiago},
  booktitle={Security \& Privacy},
  pages={327--345},
  year={2023},
}
%
  organization={IEEE}


@inproceedings{ijcai2022p766,
  title     = {Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey},
  author    = {Fioretto, Ferdinando and Tran, Cuong and Van Hentenryck, Pascal and Zhu, Keyu},
  booktitle = {International Joint Conference on
               Artificial Intelligence},
  
  pages     = {5470--5477},
  year      = {2022},
  month     = {7},
  
}
%publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
note      = {Survey Track},
  doi       = {10.24963/ijcai.2022/766},
  url       = {https://doi.org/10.24963/ijcai.2022/766},


@article{accfairtradeoff,
author = {Pinzon, Carlos and Palamidessi, Catuscia and Piantanida, Pablo and Valencia, Frank},
year = {2023},
month = {05},
pages = {1-30},
title = {On the incompatibility of accuracy and equal opportunity},
journal = {Machine Learning},
}
%
doi = {10.1007/s10994-023-06331-y}

@article{rodolfa2021empirical,
  title={Empirical observation of negligible fairness--accuracy trade-offs in machine learning for public policy},
  author={Rodolfa, Kit T and Lamba, Hemank and Ghani, Rayid},
  journal={Nature Machine Intelligence},
  volume={3},
  number={10},
  pages={896--904},
  year={2021},
}
%
  publisher={Nature Publishing Group UK London}

@article{zhai2022understanding,
  title={Understanding why generalized reweighting does not improve over ERM},
  author={Zhai, Runtian and Dan, Chen and Kolter, Zico and Ravikumar, Pradeep},
  booktitle={International Conference on Learning Representation},
  year={2023}
}

@article{
veldanda2022fairness,
title={Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss},
author={Akshaj Kumar Veldanda and Ivan Brugere and Jiahao Chen and Sanghamitra Dutta and Alan Mishler and Siddharth Garg},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},

}
%url={https://openreview.net/forum?id=f4VyYhkRvi},
note={}

%  general
% url = {https://arxiv.org/abs/2206.10923},
@misc{arxivmichael,
  doi = {10.48550/ARXIV.2206.10923},
  author = {Maheshwari, Gaurav and Perrot, Michaël},
  title = {FairGrad: Fairness Aware Gradient Descent},
  publisher = {arXiv},
  year = {2022},
}


@InProceedings{classIMb1,
  title = 	 {Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding},
  author =       {Guo, Lan-Zhe and Li, Yu-Feng},
  booktitle = 	 {International Conference on Machine Learning},
  pages = 	 {8082--8094},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},  
  month = 	 {17--23 Jul},

}
%
  pdf = 	 {https://proceedings.mlr.press/v162/guo22e/guo22e.pdf},
  url = 	 {https://proceedings.mlr.press/v162/guo22e.html}
series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR},

@article{classIMb2,
  title={Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks},
  author={Sivaramakrishnan Rajaraman and Prasanth Ganesan and Sameer K. Antani},
  journal={PLoS ONE},
  year={2021},
  volume={17},
}
%
  url={https://api.semanticscholar.org/CorpusID:238259577}

@misc{classIMb3,
	author = {Jason Brownlee},
	title = {{A} {G}entle {I}ntroduction to {T}hreshold-{M}oving for {I}mbalanced {C}lassification - {M}achine{L}earning{M}astery.com},
	year = {},
	note = {[Accessed 31-08-2023]},
} 
%issn = {0022-0000},
%url = {https://www.sciencedirect.com/science/article/pii/S002200009791504X},
@article{saddlepointsolve,
title = {A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting},
journal = {Journal of Computer and System Sciences},
volume = {55},
number = {1},
pages = {119-139},
year = {1997},
author = {Yoav Freund and Robert E Schapire}
}
%
doi = {10.1006/jcss.1997.1504},


%isbn = {1595933832},
%address = {New York, NY, USA},
@inproceedings{curves,
author = {Davis, Jesse and Goadrich, Mark},
title = {The Relationship between Precision-Recall and ROC Curves},
year = {2006},
booktitle = {International Conference on Machine Learning},
pages = {233–240},

}
%
publisher = {Association for Computing Machinery},
doi = {10.1145/1143844.1143874},
location = {Pittsburgh, Pennsylvania, USA},
series = {ICML '06}

@inproceedings{cormode,
author = {Cormode, Graham},
title = {Personal Privacy vs Population Privacy: Learning to Attack Anonymization},
year = {2011},
booktitle = {International Conference on Knowledge Discovery and Data Mining},
pages = {1253–1261},
}
%doi = {10.1145/2020408.2020598},
%location = {San Diego, California, USA},
series = {KDD '11}
%publisher = {Association for Computing Machinery},

%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
%issn = {0360-0300},
%url = {https://doi.org/10.1145/3457607},
@article{surveyfair,
author = {Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram},
title = {A Survey on Bias and Fairness in Machine Learning},
year = {2021},
volume = {54},
number = {6},
journal = {Comput. Surv.},
month = {jul},
articleno = {115},
numpages = {35},
}
%
doi = {10.1145/3457607},

@article{attinfSocial1,
author = {Gong, Neil Zhenqiang and Talwalkar, Ameet and Mackey, Lester and Huang, Ling and Shin, Eui Chul Richard and Stefanov, Emil and Shi, Elaine (Runting) and Song, Dawn},
title = {Joint Link Prediction and Attribute Inference Using a Social-Attribute Network},
year = {2014},
volume = {5},
number = {2},
journal = {Trans. Intell. Syst. Technol.},
}
%
doi = {10.1145/2594455},
publisher = {Association for Computing Machinery},

%address = {New York, NY, USA},

%issn = {2471-2566},
%url = {https://doi.org/10.1145/3154793},
%numpages = {30},
%%month = {jan},
@article{attinfSocial2,
author = {Gong, Neil Zhenqiang and Liu, Bin},
title = {Attribute Inference Attacks in Online Social Networks},
year = {2018},
volume = {21},
number = {1},
journal = {Trans. Priv. Secur.},
articleno = {3},
}
%
doi = {10.1145/3154793},
publisher = {Association for Computing Machinery},

%isbn = {978-1-931971-32-4},
%address = {Austin, TX},
%url = {https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/gong},
%publisher = {USENIX Association},
%month = aug,
@inproceedings {attinfSocial3,
author = {Neil Zhenqiang Gong and Bin Liu},
title = {You Are Who You Know and How You Behave: Attribute Inference Attacks via Users{\textquoteright} Social Friends and Behaviors},
booktitle = {USENIX Security Symposium },
year = {2016},
pages = {979--995},
}


  %URL = {https://hal.inria.fr/hal-00748162},
  %ADDRESS = {San Diego, United States},
  %MONTH = Feb,
@inproceedings{attinfSocial4,
  TITLE = {{You Are What You Like! Information Leakage Through Users' Interests}},
  YEAR = {2012},
  AUTHOR = {Chaabane, Abdelberi and Acs, Gergely and Kaafar, Mohamed Ali},
  BOOKTITLE = {Network and Distributed System Security Symposium},
  PAGES = {1-14},
}




@inproceedings{attinfSocial5,
  author={Elena Zheleva and Lise Getoor},
  title={To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles},
  year={2009},
  BOOKTITLE = {International Conference on World Wide Web},
  pages={531-540},
  doi={10.1145/1526709.1526781},
}



%isbn = {9781450349130},
%publisher = {International World Wide Web Conferences Steering Committee},
%address = {Republic and Canton of Geneva, CHE},
%url = {https://doi.org/10.1145/3038912.3052695},
@inproceedings{attinfSocial6,
author = {Jia, Jinyuan and Wang, Binghui and Zhang, Le and Gong, Neil Zhenqiang},
title = {AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields},
year = {2017},
booktitle = {International Conference on World Wide Web},
pages = {1561–1569},
location = {Perth, Australia},
series = {WWW '17}
}
%
doi = {10.1145/3038912.3052695},
  

%isbn = {9781450382878},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
@inbook{dysan,
author = {Boutet, Antoine and Frindel, Carole and Gambs, S\'{e}bastien and Jourdan, Th\'{e}o and Ngueveu, Rosin Claude},
title = {DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks},
year = {2021},
doi = {10.1145/3433210.3453095},
booktitle = {Asia Conference on Computer and Communications Security},
pages = {672–686},
}
%serie = {ASIA CCS '21}

@inproceedings{attprivacy,
author = {Zhang, Wanrong and Ohrimenko, Olga and Cummings, Rachel},
title = {Attribute Privacy: Framework and Mechanisms},
year = {2022},
isbn = {9781450393522},
booktitle = {Fairness, Accountability, and Transparency},
pages = {757–766},
numpages = {10},

}
%url = {https://doi.org/10.1145/3531146.3533139},
doi = {10.1145/3531146.3533139},
keywords = {Pufferfish privacy, attribute privacy, formal privacy frameworks, privacy-preserving mechanisms},
series = {FAccT '22}
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},


%differential privacy and fairness
@inproceedings{dispvuln,
author    = {Mohammad Yaghini and  Bogdan Kulynych and Carmela Troncoso},
title     = {Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning},
year      = {2022},
booktitle = {Privacy Enhancing Technologies Symposium}
}


%isbn = {9781450391405},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
@inproceedings{GongMIAUnfair,
author = {Zhong, Da and Sun, Haipei and Xu, Jun and Gong, Neil and Wang, Wendy Hui},
title = {Understanding Disparate Effects of Membership Inference Attacks and Their Countermeasures},
year = {2022},
booktitle = {Asia Conference on Computer and Communications Security},
pages = {959–974},
}
%location = {Nagasaki, Japan},
series = {ASIA CCS '22}
doi = {10.1145/3488932.3501279},


%sbn = {9781450311151},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
%url = {https://doi.org/10.1145/2090236.2090255},
@inproceedings{indivfairness,
author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Richard},
title = {Fairness through Awareness},
year = {2012},
booktitle = {Innovations in Theoretical Computer Science},
pages = {214–226},
}
%doi = {10.1145/2090236.2090255},
location = {Cambridge, Massachusetts},
series = {ITCS '12}

@inproceedings{outIndist,
author = {Dwork, Cynthia and Kim, Michael P. and Reingold, Omer and Rothblum, Guy N. and Yona, Gal},
title = {Outcome indistinguishability},
year = {2021},
booktitle = {Symposium on Theory of Computing},
pages = {1095–1108},
numpages = {14},
}
%isbn = {9781450380539},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3406325.3451064},
doi = {10.1145/3406325.3451064},
keywords = {Prediction, Fairness, Computational Indistinguishability},
location = {Virtual, Italy},
series = {STOC 2021}  



%isbn = {9781450369367},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
%url = {https://doi.org/10.1145/3351095.3372872},
@inproceedings{dpfair,
author = {Pujol, David and McKenna, Ryan and Kuppam, Satya and Hay, Michael and Machanavajjhala, Ashwin and Miklau, Gerome},
title = {Fair Decision Making Using Privacy-Protected Data},
year = {2020},
booktitle = {Fairness, Accountability, and Transparency},
pages = {189–199},
}
%
doi = {10.1145/3351095.3372872},
location = {Barcelona, Spain},
series = {FAT* '20}

%url={https://ojs.aaai.org/index.php/AAAI/article/view/17193}, 
%month={May}, 
@article{fairprivatelagrangian, 
title={Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach}, 
volume={35}, 
number={11}, 
journal={AAAI Conference on Artificial Intelligence}, 
author={Tran, Cuong and Fioretto, Ferdinando and Van Hentenryck, Pascal}, 
year={2021}, 
pages={9932-9939} 
}

%editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  %series = 	 {Proceedings of Machine Learning Research},
  %month = 	 {09--15 Jun},
  %publisher =    {PMLR},
  %pdf = 	 {http://proceedings.mlr.press/v97/jagielski19a/jagielski19a.pdf},
  %url = 	 {https://proceedings.mlr.press/v97/jagielski19a.html}
@InProceedings{dpfairlearn,
  title = 	 {Differentially Private Fair Learning},
  author =       {Jagielski, Matthew and Kearns, Michael and Mao, Jieming and Oprea, Alina and Roth, Aaron and -Malvajerdi, Saeed Sharifi and Ullman, Jonathan},
  booktitle = 	 {International Conference on Machine Learning},
  pages = 	 {3000--3008},
  year = 	 {2019},
  volume = 	 {97},
}

@incollection{dpaccdisp,
title = {Differential Privacy Has Disparate Impact on Model Accuracy},
author = {Bagdasaryan, Eugene and Poursaeed, Omid and Shmatikov, Vitaly},
booktitle = {Advances in Neural Information Processing Systems},
pages = {15479--15488},
year = {2019}}

%isbn = {978-1-939133-06-9},
%address = {Santa Clara, CA},
%url = {https://www.usenix.org/conference/usenixsecurity19/presentation/jayaraman},
%publisher = {USENIX Association},
%month = aug,
@inproceedings {dpVacc,
author = {Bargav Jayaraman and David Evans},
title = {Evaluating Differentially Private Machine Learning in Practice},
booktitle = {USENIX Security Symposium},
year = {2019},
pages = {1895--1912},
}

%isbn = {9781450367110},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
%url = {https://doi.org/10.1145/3314183.3323847},
@inproceedings{cummings,
author = {Cummings, Rachel and Gupta, Varun and Kimpara, Dhamma and Morgenstern, Jamie},
title = {On the Compatibility of Privacy and Fairness},
year = {2019},
booktitle = {Conference on User Modeling, Adaptation and Personalization},
pages = {309–315},

}
%doi = {10.1145/3314183.3323847},
series = {UMAP'19 Adjunct}
location = {Larnaca, Cyprus},

@techreport{ec2019ethics,
  address = {Brussels},
  author = {{High-Level Expert Group on AI}},
  institution = {European Commission},
  language = {eng},
  month = apr,
  title = {Ethics guidelines for trustworthy AI},
  type = {Report},
  year = {2019}
}
%
  url = {https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai},

@inproceedings{nist,
  title={A Taxonomy and Terminology of Adversarial Machine Learning},
  author={Elham Tabassi and Kevin J. Burns and M. Hadjimichael and Andres Molina-Markham and Julian Sexton},
  year={2019},
  booktitle = {NIST Interagency/Internal Report}
}
%
  url = {https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf},

@inproceedings{dpia, 
title={Art. 35 {GDPR} Data protection impact assessment},
url={https://gdpr-info.eu/art-35-gdpr/}, 
author={European Union Law},
year={2018},
booktitle={General Data Protection Regulation (GDPR)} }

@article{ico, 
title={{AI} auditing and impact assessment: according to the UK information commissioner’s office}, 
journal={AI and Ethics}, 
author={Kazim, Emre and Denny, Danielle Mendes Thame and Koshiyama, Adriano}, 
year={2021}, 
month={Feb} }
%ISSN={2730-5953, 2730-5961}, url={http://link.springer.com/10.1007/s43681-021-00039-2}, DOI={10.1007/s43681-021-00039-2}, 

@inproceedings{whitehouse, 
title={Guidance for Regulation of Artificial Intelligence Applications},
author={White House},
year = {2020},
booktitle={Memorandum For The Heads Of Executive Departments And Agencies} }
%url={https://www.whitehouse.gov/wp-content/uploads/2020/11/M-21-06.pdf}, 
%metrics

@INPROCEEDINGS{memprivNattpriv,
  author={Zhao, Benjamin Zi Hao and Agrawal, Aviral and Coburn, Catisha and Asghar, Hassan Jameel and Bhaskar, Raghav and Kaafar, Mohamed Ali and Webb, Darren and Dickinson, Peter},
  booktitle={European Security \& Privacy}, 
  title={On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models}, 
  year={2021},
  pages={232-251},
  doi={10.1109/EuroSP51992.2021.00025}
}

@article{duddu2023sok,
  title={SoK: Unintended Interactions among Machine Learning Defenses and Risks},
  author={Duddu, Vasisht and Szyller, Sebastian and Asokan, N},
  journal={arXiv preprint arXiv:2312.04542},
  year={2023}
}


@inproceedings{suri2023dissecting,
  title={Dissecting distribution inference},
  author={Suri, Anshuman and Lu, Yifu and Chen, Yanjin and Evans, David},
  booktitle={Conference on Secure and Trustworthy Machine Learning},
  pages={150--164},
  year={2023},
}
%
  organization={IEEE}


@article{de2020overview,
  title={An overview of privacy in machine learning},
  author={De Cristofaro, Emiliano},
  journal={arXiv preprint arXiv:2005.08679},
  year={2020}
}


@article{pate2021fairness,
  title={A Fairness Analysis on Private Aggregation of Teacher Ensembles},
  author={Tran, Cuong and Dinh, My H and Beiter, Kyle and Fioretto, Ferdinando},
  journal={arXiv preprint arXiv:2109.08630},
  year={2021}
}

@article{fioretto2022differential,
  title={Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey},
  author={Fioretto, Ferdinando and Tran, Cuong and Van Hentenryck, Pascal and Zhu, Keyu},
  journal={arXiv preprint arXiv:2202.08187},
  year={2022}
}


% attribute inference attacks in ML
%publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
%address = "United States",
@inproceedings{zhao2021infeasibility,
title = "On the (in)feasibility of attribute inference attacks on machine learning models",
author = "Zhao, {Benjamin Zi Hao} and Aviral Agrawal and Catisha Coburn and Asghar, {Hassan Jameel} and Raghav Bhaskar and Kaafar, {Mohamed Ali} and Darren Webb and Peter Dickinson",
year = "2021",
pages = "232--251",
booktitle = "European Security \& Privacy",
}
%
doi = "10.1109/EuroSP51992.2021.00025",
serie = {EuroS&P '2021},

%isbn = {978-1-939133-31-1},
%address = {Boston, MA},
%url = {https://www.usenix.org/conference/usenixsecurity22/presentation/mehnaz},
%publisher = {USENIX Association},
%month = aug,
@inproceedings{MehnazAttInf,
author = {Shagufta Mehnaz and Sayanton V. Dibbo and Ehsanul Kabir and Ninghui Li and Elisa Bertino},
title = {Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models},
booktitle = {USENIX Security Symposium},
year = {2022},
pages = {4579--4596},
}

%isbn = {9781450338325},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
%%url = {https://doi.org/10.1145/2810103.2813677},
@inproceedings{fredrikson1,
author = {Fredrikson, Matt and Jha, Somesh and Ristenpart, Thomas},
title = {Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures},
year = {2015},
booktitle = {Conference on Computer and Communications Security},
pages = {1322–1333},

}
%
doi = {10.1145/2810103.2813677},
location = {Denver, Colorado, USA},
series = {CCS '15}


%isbn = {9781931971157},
@inproceedings{fredrikson2,
author = {Fredrikson, Matthew and Lantz, Eric and Jha, Somesh and Lin, Simon and Page, David and Ristenpart, Thomas},
title = {Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing},
year = {2014},
booktitle = {USENIX Conference on Security Symposium},
pages = {17–32},
}
%
location = {San Diego, CA},
series = {SEC'14}

@inproceedings{Song2020Overlearning,
title={Overlearning Reveals Sensitive Attributes},
author={Congzheng Song and Vitaly Shmatikov},
booktitle={International Conference on Learning Representations},
year={2020}
}


%isbn = {9781450384544},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
%url = {https://doi.org/10.1145/3460120.3484533},
@inproceedings{malekzadeh2021honestbutcurious,
author = {Malekzadeh, Mohammad and Borovykh, Anastasia and G\"{u}nd\"{u}z, Deniz},
title = {Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs},
year = {2021},
booktitle = {Conference on Computer and Communications Security},
pages = {825–844},
}
%location = {Virtual Event, Republic of Korea},
series = {CCS '21}
doi = {10.1145/3460120.3484533},

@article{jayaraman2022attribute,
  title={Are Attribute Inference Attacks Just Imputation?},
  author={Jayaraman, Bargav and Evans, David},
  journal={arXiv preprint arXiv:2209.01292},
  year={2022}
}


@inproceedings{Mahajan2020DoesLS,
  title={Does Learning Stable Features Provide Privacy Benefits for Machine Learning Models?},
  author={Divyat Mahajan, Shruti Tople, Amit Sharma},
  booktitle = {NeurIPS PPML Workshop},
  year={2020}
}

@inproceedings{Malekzadeh_2021,
	 
	year = 2021,	month = {nov},  
	author = {Mohammad Malekzadeh and Anastasia Borovykh and Deniz Gündüz},  
	title = {Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers{\textquotesingle} Outputs},  
	booktitle = {Conference on Computer and Communications Security}}
%
	publisher = {{ACM}},  
doi = {10.1145/3460120.3484533},  
	url = {https://doi.org/10.1145%2F3460120.3484533}, 


@INPROCEEDINGS{meminf,
  author={Shokri, Reza and Stronati, Marco and Song, Congzheng and Shmatikov, Vitaly},
  booktitle={Security \& Privacy}, 
  title={Membership Inference Attacks Against Machine Learning Models}, 
  year={2017},
  pages={3-18},}
%
  doi={10.1109/SP.2017.41}
  
@article{chang2021privacy,
  title={On the Privacy Risks of Algorithmic Fairness},
  author={Hongyang Chang and R. Shokri},
  journal={European Security \& Privacy},
  year={2021},
  pages={292-303}
}

@article{duddu2022inferring,
  title={Inferring Sensitive Attributes from Model Explanations},
  author={Duddu, Vasisht and Boutet, Antoine},
  journal={arXiv preprint arXiv:2208.09967},
  year={2022}
}

%editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 %publisher = {Curran Associates, Inc.},
 %url = {https://proceedings.neurips.cc/paper/2020/file/6b8b8e3bd6ad94b985c1b1f1b7a94cb2-Paper.pdf},
@inproceedings{NEURIPS2020_6b8b8e3b,
 author = {Zhao, Han and Chi, Jianfeng and Tian, Yuan and Gordon, Geoffrey J},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {9485--9496},
 title = {Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation},
 volume = {33},
 year = {2020}
}



@ARTICLE{8515092,
author={S. A. {Osia} and A. {Taheri} and A. S. {Shamsabadi} and K. {Katevas} and H. {Haddadi} and H. R. {Rabiee}},
journal={Transactions on Knowledge and Data Engineering},
title={Deep Private-Feature Extraction},
year={2020},
volume={32},
number={1},
pages={54-66},
}



%eprint    = {1707.00075}
@article{advfair,
  author    = {Alex Beutel and Jilin Chen and Zhe Zhao and Ed H. Chi},
  title     = {Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations},
  year      = {2017},
  publisher = {arXiv},
  doi = {10.48550/ARXIV.1707.00075},
}

%property inference attack

@article{propinf,
  title={Dataset-Level Attribute Leakage in Collaborative Learning},
  author={Zhang, Wanrong and Tople, Shruti and Ohrimenko, Olga},
  journal={arXiv:2006.07267},
  year={2020}
}

%month = sep,
@article{propinf2,
author = {Ateniese, Giuseppe and Mancini, Luigi V. and Spognardi, Angelo and Villani, Antonio and Vitali, Domenico and Felici, Giovanni},
title = {Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers},
year = {2015},
volume = {10},
number = {3},
journal = {Int. J. Secur. Netw.},
pages = {137–150}
}


%isbn = {9781450356930},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
%url = {https://doi.org/10.1145/3243734.3243834},
@inproceedings{propinf3,
author = {Ganju, Karan and Wang, Qi and Yang, Wei and Gunter, Carl A. and Borisov, Nikita},
title = {Property Inference Attacks on Fully Connected Neural Networks Using Permutation Invariant Representations},
year = {2018},
booktitle = {Conference on Computer and Communications Security},
pages = {619–633},

}
%location = {Toronto, Canada},
series = {CCS '18}
doi = {10.1145/3243734.3243834},


@article{propinf4,
  title={Formalizing and Estimating Distribution Inference Risks},
  author={Suri, Anshuman and Evans, David},
  journal={Privacy Enhancing Technologies},
  year={2022}
}

@inproceedings{fedinference,
author={L. {Melis} and C. {Song} and E. {De Cristofaro} and V. {Shmatikov}},
booktitle={Security \& Privacy},
title={Exploiting Unintended Feature Leakage in Collaborative Learning},
year={2019},
pages={691-706}
}

@INPROCEEDINGS {ferryExploit,
author = {J. Ferry and U. Aivodji and S. Gambs and M. Huguet and M. Siala},
booktitle = {Conference on Secure and Trustworthy Machine Learning},
title = {Exploiting Fairness to Enhance Sensitive Attributes Reconstruction},
year = {2023},
volume = {},
issn = {},
pages = {18-41},
month = {feb}
}
%keywords = {training;measurement;learning systems;privacy;pipelines;training data;machine learning},
doi = {10.1109/SaTML54575.2023.00012},
url = {https://doi.ieeecomputersociety.org/10.1109/SaTML54575.2023.00012},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},



% defences against attribute inference attacks

@inproceedings{10.5555/3042817.3042973,
author = {Zemel, Richard and Wu, Yu and Swersky, Kevin and Pitassi, Toniann and Dwork, Cynthia},
title = {Learning Fair Representations},
year = {2013},
booktitle = {International Conference on Machine Learning},
}
%serie = {ICML '13},

%month = jan,
@article{10.5555/3122009.3208010,
author = {Hamm, Jihun},
title = {Minimax Filter: Learning to Preserve Privacy from Inference Attacks},
year = {2017},
volume = {18},
number = {1},
journal = {J. Mach. Learn. Res.},
pages = {4704–4734}
}

@inproceedings{10.5555/3327546.3327583,
author = {Moyer, Daniel and Gao, Shuyang and Brekelmans, Rob and Steeg, Greg Ver and Galstyan, Aram},
title = {Invariant Representations without Adversarial Training},
year = {2018},
booktitle = {Advances in Neural Information Processing Systems}
}

@inproceedings{10.5555/3294771.3294827,
author = {Xie, Qizhe and Dai, Zihang and Du, Yulun and Hovy, Eduard and Neubig, Graham},
title = {Controllable Invariance through Adversarial Feature Learning},
year = {2017},
booktitle = {Advances in Neural Information Processing Systems}
}

@InProceedings{pmlr-v80-madras18a,
  title = 	 {Learning Adversarially Fair and Transferable Representations},
  author =       {Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard},
  pages = 	 {3384--3393},
  year = 	 {2018},
  volume = 	 {80},
  booktitle = 	 {Proceedings of Machine Learning Research},
}

@inproceedings{censoringadv,
title     = "Censoring Representations with an Adversary",
author    = "Harrison Edwards and Amos Storkey",
year      = "2016",
booktitle = {International Conference on Learning Representations}
}
@inproceedings{NIPS2017_48ab2f9b,
 author = {Louppe, Gilles and Kagan, Michael and Cranmer, Kyle},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Learning to Pivot with Adversarial Networks},
  volume = {30},
 year = {2017}
}
%url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/48ab2f9b45957ab574cf005eb8a76760-Paper.pdf},

%isbn = {9781450360128},
%publisher = {Association for Computing Machinery},
%address = {New York, NY, USA},
%url = {https://doi.org/10.1145/3278721.3278779},
@inproceedings{debiase,
author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
title = {Mitigating Unwanted Biases with Adversarial Learning},
year = {2018},
booktitle = {Conference on AI, Ethics, and Society},
pages = {335–340},
}
% location = {New Orleans, LA, USA},
series = {AIES '18}
doi = {10.1145/3278721.3278779},

  %month = {10},
%pages = {},
@article{preprocessing,
author = {Kamiran, Faisal and Calders, Toon},
year = {2011},
title = {Data Pre-Processing Techniques for Classification without Discrimination},
volume = {33},
journal = {Knowledge and Information Systems},
}
%doi = {10.1007/s10115-011-0463-8}

%series = 	 {Proceedings of Machine Learning Research},
  %month = 	 {10--15 Jul},
  %publisher =    {PMLR},
  %pdf = 	 {http://proceedings.mlr.press/v80/agarwal18a/agarwal18a.pdf},
  %url = 	 {https://proceedings.mlr.press/v80/agarwal18a.html},
@InProceedings{reductions,
  title = 	 {A Reductions Approach to Fair Classification},
  author =       {Agarwal, Alekh and Beygelzimer, Alina and Dudik, Miroslav and Langford, John and Wallach, Hanna},
  booktitle = 	 {International Conference on Machine Learning},
  pages = 	 {60--69},
  year = 	 {2018},
  volume = 	 {80},
}

@article{kifer2014pufferfish,
author = {Kifer, Daniel and Machanavajjhala, Ashwin},
title = {Pufferfish: A framework for mathematical privacy definitions},
year = {2014},
issue_date = {January 2014},
volume = {39},
number = {1},
issn = {0362-5915},
journal = {Trans. Database Syst.},
month = {jan},
articleno = {3},
numpages = {36},
keywords = {Privacy, differential privacy}
}
%url = {https://doi.org/10.1145/2514689},
doi = {10.1145/2514689},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},

@inproceedings{song2017pufferfish,
  title={Pufferfish privacy mechanisms for correlated data},
  author={Song, Shuang and Wang, Yizhen and Chaudhuri, Kamalika},
  booktitle={International Conference on Management of Data},
  pages={1291--1306},
  year={2017}
}

@article{grinsztajn2022tree,
  title={Why do tree-based models still outperform deep learning on typical tabular data?},
  author={Grinsztajn, L{\'e}o and Oyallon, Edouard and Varoquaux, Ga{\"e}l},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={507--520},
  year={2022}
}



@inproceedings {attriguard,
author = {Jinyuan Jia and Neil Zhenqiang Gong},
title = {AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning},
booktitle = {USENIX Security},
year = {2018},
pages = {513--529},
}


% fairness metrics

@article{fairmetric,
author  = {Muhammad Bilal Zafar and Isabel Valera and Manuel Gomez-Rodriguez and Krishna P. Gummadi},
title   = {Fairness Constraints: A Flexible Approach for Fair Classification},
journal = {Journal of Machine Learning Research},
year    = {2019},
volume  = {20},
number  = {75},
pages   = {1-42}
}

@inproceedings{fairmetric2,
author = {Hardt, Moritz and Price, Eric and Srebro, Nathan},
title = {Equality of Opportunity in Supervised Learning},
year = {2016},
booktitle = {Advances in Neural Information Processing Systems},
pages = {3323–3331}
}

@article{fairjustice,
author = {Alikhademi, Kiana and Drobina, Emma and Prioleau, Diandra and Richardson, Brianna and Purves, Duncan and Gilbert, Juan E.},
title = {A Review of Predictive Policing from the Perspective of Fairness},
year = {2022},
issue_date = {Mar 2022},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {30},
number = {1},
issn = {0924-8463},
journal = {Artif. Intell. Law},
month = {mar},
pages = {1–17},
numpages = {17},
keywords = {Predictive policing, Algorithmic fairness, Fairness, AI in criminal justice}
}
%url = {https://doi.org/10.1007/s10506-021-09286-4},
doi = {10.1007/s10506-021-09286-4},
  
@article{folk,
  title={Retiring Adult: New Datasets for Fair Machine Learning},
  author={Ding, Frances and Hardt, Moritz and Miller, John and Schmidt, Ludwig},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

@inproceedings{
    SDV,
    title={The Synthetic data vault},
    author={Patki, Neha and Wedge, Roy and Veeramachaneni, Kalyan},
    booktitle={International Conference on Data Science and Advanced Analytics},
    year={2016},
    pages={399-410},
    month={Oct}
}
%
    doi={10.1109/DSAA.2016.49},

@misc{dpbad,
  author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Rich},
  title = {Fairness Through Awareness},
        eprint={1104.3913},
      archivePrefix={arXiv},
  year = {2011},
      primaryClass={cs.CY}
}
%keywords = {Computational Complexity (cs.CC), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences},
%doi = {10.48550/ARXIV.1104.3913},  
  url = {https://arxiv.org/abs/1104.3913},
copyright = {arXiv.org perpetual, non-exclusive license}

@INPROCEEDINGS{fairlog,

  author={Radovanović, Sandro and Petrović, Andrija and Delibašić, Boris and Suknović, Milija},

  booktitle={International Conference on INnovations in Intelligent SysTems and Applications}, 

  title={Enforcing fairness in logistic regression algorithm}, 

  year={2020},

  volume={},

  number={},

  pages={1-7},
}
%doi={10.1109/INISTA49547.2020.9194676}


@misc{fairreg,
      title={Fair Regression: Quantitative Definitions and Reduction-based Algorithms}, 
      author={Alekh Agarwal and Miroslav Dudík and Zhiwei Steven Wu},
      year={2019},
      eprint={1905.12843},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}



@InProceedings{fairaudit1,
  title = 	 {Blind Justice: Fairness with Encrypted Sensitive Attributes},
  author =       {Kilbertus, Niki and Gascon, Adria and Kusner, Matt and Veale, Michael and Gummadi, Krishna and Weller, Adrian},
  booktitle = 	 {International Conference on Machine Learning},
  pages = 	 {2630--2639},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  
}
%series = 	 {Proceedings of Machine Learning Research},
pdf = 	 {http://proceedings.mlr.press/v80/kilbertus18a/kilbertus18a.pdf},
  url = 	 {https://proceedings.mlr.press/v80/kilbertus18a.html},



@inproceedings{fairaudit2,
author = {Park, Saerom and Kim, Seongmin and Lim, Yeon-sup},
title = {Fairness Audit of Machine Learning Models with Confidential Computing},
year = {2022},
isbn = {9781450390965},
booktitle = {Web Conference 2022},
pages = {3488–3499},
numpages = {12},
keywords = {Confidential computing, Algorithmic audit, Security and privacy, Fairness},
}
%publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3485447.3512244},
doi = {10.1145/3485447.3512244},
location = {Virtual Event, Lyon, France},
series = {WWW '22}

@inproceedings{fairaudit3,
author = {Segal, Shahar and Adi, Yossi and Pinkas, Benny and Baum, Carsten and Ganesh, Chaya and Keshet, Joseph},
title = {Fairness in the Eyes of the Data: Certifying Machine-Learning Models},
year = {2021},
booktitle = {Conference on AI, Ethics, and Society},
pages = {926–935},
numpages = {10},
keywords = {machine-learning, cryptography, privacy, fairness},
}  
%publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3461702.3462554},
doi = {10.1145/3461702.3462554},
location = {Virtual Event, USA},
series = {AIES '21}
isbn = {9781450384735},

@article{yadav2024fairproof,
  title={FairProof: Confidential and Certifiable Fairness for Neural Networks},
  author={Yadav, Chhavi and Chowdhury, Amrita Roy and Boneh, Dan and Chaudhuri, Kamalika},
  journal={arXiv preprint arXiv:2402.12572},
  year={2024}
}

@inproceedings{khedr2023certifair,
  title={Certifair: A framework for certified global fairness of neural networks},
  author={Khedr, Haitham and Shoukry, Yasser},
  booktitle={AAAI Conference on Artificial Intelligence},
  volume={37},
  number={7},
  pages={8237--8245},
  year={2023}
}

@article{urban20,
author = {Urban, Caterina and Christakis, Maria and W\"{u}stholz, Valentin and Zhang, Fuyuan},
title = {Perfectly parallel fairness certification of neural networks},
year = {2020},
issue_date = {November 2020},
volume = {4},
number = {OOPSLA},
journal = {Program. Lang.},
month = {nov},
articleno = {185},
numpages = {30},
keywords = {Static Analysis, Neural Networks, Fairness, Abstract Interpretation}
}
%publisher = {Association for Computing Machinery},
address = {New York, NY, USA},

@inproceedings{
chugg2023auditing,
title={Auditing Fairness by Betting},
author={Ben Chugg and Santiago Cortes-Gomez and Bryan Wilder and Aaditya Ramdas},
booktitle={Conference on Neural Information Processing Systems},
year={2023},
}
%
url={https://openreview.net/forum?id=EEVpt3dJQj}

@inproceedings{yan2022active,
  title={Active fairness auditing},
  author={Yan, Tom and Zhang, Chicheng},
  booktitle={International Conference on Machine Learning},
  pages={24929--24962},
  year={2022},
  organization={PMLR}
}

@article{de2024fairness,
  title={Fairness Auditing with Multi-Agent Collaboration},
  author={de Vos, Martijn and Dhasade, Akash and Bourr{\'e}e, Jade Garcia and Kermarrec, Anne-Marie and Merrer, Erwan Le and Rottembourg, Benoit and Tredan, Gilles},
  journal={arXiv preprint arXiv:2402.08522},
  year={2024}
}

@inproceedings{ghosh2022algorithmic,
  title={Algorithmic fairness verification with graphical models},
  author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S},
  booktitle={AAAI Conference on Artificial Intelligence},
  volume={36},
  number={9},
  pages={9539--9548},
  year={2022}
}

@inproceedings{ghosh2023biased,
  title={“How Biased are Your Features?”: Computing Fairness Influence Functions with Global Sensitivity Analysis},
  author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S},
  booktitle={Fairness, Accountability, and Transparency},
  pages={138--148},
  year={2023}
}

@article{FairSquare,
author = {Albarghouthi, Aws and D'Antoni, Loris and Drews, Samuel and Nori, Aditya V.},
title = {FairSquare: probabilistic verification of program fairness},
year = {2017},
issue_date = {October 2017},
volume = {1},
number = {OOPSLA},
journal = {Program. Lang.},
month = {oct},
articleno = {80},
numpages = {30},
keywords = {Algorithmic Fairness, Probabilistic Inference, Probabilistic Programming}
}
%publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3133904},
doi = {10.1145/3133904},

@article{saleiro2018aequitas,
  title={Aequitas: A bias and fairness audit toolkit},
  author={Saleiro, Pedro and Kuester, Benedict and Hinkson, Loren and London, Jesse and Stevens, Abby and Anisfeld, Ari and Rodolfa, Kit T and Ghani, Rayid},
  journal={arXiv preprint arXiv:1811.05577},
  year={2018}
}

@article{bastani2019probabilistic,
  title={Probabilistic verification of fairness properties via concentration},
  author={Bastani, Osbert and Zhang, Xin and Solar-Lezama, Armando},
  journal={Programming Languages},
  volume={3},
  number={OOPSLA},
  pages={1--27},
  year={2019},
}
%
  publisher={ACM New York, NY, USA}

@article{adler2018auditing,
  title={Auditing black-box models for indirect influence},
  author={Adler, Philip and Falk, Casey and Friedler, Sorelle A and Nix, Tionney and Rybeck, Gabriel and Scheidegger, Carlos and Smith, Brandon and Venkatasubramanian, Suresh},
  journal={Knowledge and Information Systems},
  volume={54},
  pages={95--122},
  year={2018},
}
%  publisher={Springer}

@inproceedings{black2020fliptest,
  title={Fliptest: fairness testing via optimal transport},
  author={Black, Emily and Yeom, Samuel and Fredrikson, Matt},
  booktitle={Fairness, Accountability, and Transparency},
  pages={111--121},
  year={2020}
}

@article{Justicia, 
title={Justicia: A Stochastic SAT Approach to Formally Verify Fairness}, 
volume={35}, 
number={9}, journal={Conference on Artificial Intelligence}, author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S.}, year={2021}, month={May}, pages={7554-7563} }
%url={https://ojs.aaai.org/index.php/AAAI/article/view/16925}, DOI={10.1609/aaai.v35i9.16925}, 
}

####################AIA
@article{ofverstedt2022fast,
  title={Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment},
  author={{\"O}fverstedt, Johan and Lindblad, Joakim and Sladoje, Nata{\v{s}}a},
  journal={Pattern Recognition Letters},
  volume={159},
  pages={196--203},
  year={2022},
  publisher={Elsevier}
}


@article{dai2022comprehensive,
  title={A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability},
  author={Dai, Enyan and Zhao, Tianxiang and Zhu, Huaisheng and Xu, Junjie and Guo, Zhimeng and Liu, Hui and Tang, Jiliang and Wang, Suhang},
  journal={arXiv preprint arXiv:2204.08570},
  year={2022}
}

@article{franco2021toward,
  title={Toward learning trustworthily from data combining privacy, fairness, and explainability: an application to face recognition},
  author={Franco, Danilo and Oneto, Luca and Navarin, Nicol{\`o} and Anguita, Davide},
  journal={Entropy},
  volume={23},
  number={8},
  pages={1047},
  year={2021},
  publisher={MDPI}
}

@article{grant2020show,
  title={Show us the data: Privacy, explainability, and why the law can't have both},
  author={Grant, Thomas D and Wischik, Damon J},
  journal={Geo. Wash. L. Rev.},
  volume={88},
  pages={1350},
  year={2020},
  publisher={HeinOnline}
}

@article{pielke2005hurricanes,
  title={Hurricanes and global warming},
  author={Pielke Jr, Roger A and Landsea, Chris and Mayfield, Max and Layer, J and Pasch, Richard},
  journal={Bulletin of the American Meteorological Society},
  volume={86},
  number={11},
  pages={1571--1576},
  year={2005},
  publisher={American Meteorological Society}
}
@article{khasnis2005global,
  title={Global warming and infectious disease},
  author={Khasnis, Atul A and Nettleman, Mary D},
  journal={Archives of medical research},
  volume={36},
  number={6},
  pages={689--696},
  year={2005},
  publisher={Elsevier}
}
@article{houghton2005global,
  title={Global warming},
  author={Houghton, John},
  journal={Reports on progress in physics},
  volume={68},
  number={6},
  pages={1343},
  year={2005},
  publisher={IOP Publishing}
}

@misc{mcnutt2013climate,
  title={Climate change impacts},
  author={McNutt, Marcia},
  journal={Science},
  volume={341},
  number={6145},
  pages={435--435},
  year={2013},
  publisher={American Association for the Advancement of Science}
}

@article{melillo2014climate,
  title={Climate change impacts in the United States},
  author={Melillo, Jerry M and Richmond, TT and Yohe, Gary and others},
  journal={Third national climate assessment},
  volume={52},
  pages={150--174},
  year={2014},
  publisher={US Global Change Research Program, Washington, DC}
}


@misc{carbonfr,
    title={L’empreinte carbone de la France de 1995 à 2022},
    howpublished={\url{https://www.statistiques.developpement-durable.gouv.fr/lempreinte-carbone-de-la-france-de-1995-2022}},
    note={Dernier accès: 2024-09-18}
}

@article{shadrin2019designing,
  title={Designing future precision agriculture: Detection of seeds germination using artificial intelligence on a low-power embedded system},
  author={Shadrin, Dmitrii and Menshchikov, Alexander and Ermilov, Dmitry and Somov, Andrey},
  journal={IEEE Sensors Journal},
  volume={19},
  number={23},
  pages={11573--11582},
  year={2019},
  publisher={IEEE}
}
@article{schwartz2020green,
  title={Green ai},
  author={Schwartz, Roy and Dodge, Jesse and Smith, Noah A and Etzioni, Oren},
  journal={Communications of the ACM},
  volume={63},
  number={12},
  pages={54--63},
  year={2020},
  publisher={ACM New York, NY, USA}
}


@article{verdecchia2023systematic,
  title={A systematic review of Green AI},
  author={Verdecchia, Roberto and Sallou, June and Cruz, Lu{\'\i}s},
  journal={Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
  volume={13},
  number={4},
  pages={e1507},
  year={2023},
  publisher={Wiley Online Library}
}



@misc{google_vision,
  title = {Google vision },
  howpublished = {https://cloud.google.com/vision},
  note = {Accessed: 2021-05-27}
}
@article{ylc,
    title="Gradient-Based Learning Applied to Document Recognition",
    author="Y. LeCun, L. Bottou, Y. Bengio and P. Haffner",
    journal="Proceedings of the IEEE",
    volume="86",
    number="11",
    pages="2278-2324",
    year="1998"
}
@misc{RGPD,
    title="Le règlement général sur la protection des données",
    howpublished="https://www.cnil.fr/fr/reglement-europeen-protection-donnees"
}
@misc{78-17,
    title="Loi n° 78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés",
    howpublished="https://www.legifrance.gouv.fr/loda/id/LEGITEXT000006068624/2019-06-04/"
}
@misc{art,
    title="Adversarial robustness toolbox",
    howpublished="https://adversarial-robustness-toolbox.org/"
}
@misc{hardt2016equality,
    title={Equality of Opportunity in Supervised Learning},
    author={Moritz Hardt and Eric Price and Nathan Srebro},
    year={2016},
    eprint={1610.02413},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
@misc{chang2021privacy,
      title={On the Privacy Risks of Algorithmic Fairness}, 
      author={Hongyan Chang and Reza Shokri},
      year={2021},
      eprint={2011.03731},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
@misc{agarwal2018reductions,
      title={A Reductions Approach to Fair Classification}, 
      author={Alekh Agarwal and Alina Beygelzimer and Miroslav Dudík and John Langford and Hanna Wallach},
      year={2018},
      eprint={1803.02453},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{yeom,
      title={Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting}, 
      author={Samuel Yeom and Irene Giacomelli and Matt Fredrikson and Somesh Jha},
      year={2018},
      eprint={1709.01604},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}
@article{saddlepointsolve,
title = {A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting},
journal = {Journal of Computer and System Sciences},
volume = {55},
number = {1},
pages = {119-139},
year = {1997},
issn = {0022-0000},
doi = {https://doi.org/10.1006/jcss.1997.1504},
url = {https://www.sciencedirect.com/science/article/pii/S002200009791504X},
author = {Yoav Freund and Robert E Schapire},
abstract = {In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update Littlestone–Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games, and prediction of points in Rn. In the second part of the paper we apply the multiplicative weight-update technique to derive a new boosting algorithm. This boosting algorithm does not require any prior knowledge about the performance of the weak learning algorithm. We also study generalizations of the new boosting algorithm to the problem of learning functions whose range, rather than being binary, is an arbitrary finite set or a bounded segment of the real line.}
}
@misc{surveyfair,
    title={A Survey on Bias and Fairness in Machine Learning},
    author={Ninareh Mehrabi and Fred Morstatter and Nripsuta Saxena and Kristina Lerman and Aram Galstyan},
    year={2019},
    eprint = {1908.09635}
}

@inproceedings{10.1145/2020408.2020598,
author = {Cormode, Graham},
title = {Personal Privacy vs Population Privacy: Learning to Attack Anonymization},
year = {2011},
booktitle = {KDD},
pages = {1253–1261},
}

@inproceedings{abadi2016deep,
  title={Deep learning with differential privacy},
  author={Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li},
  booktitle={Proceedings of the 2016 ACM SIGSAC conference on computer and communications security},
  pages={308--318},
  year={2016}
}


@inproceedings{10.1145/3433210.3453095,
author = {Boutet, Antoine and Frindel, Carole and Gambs, S\'{e}bastien and Jourdan, Th\'{e}o and Ngueveu, Rosin Claude},
title = {DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks},
year = {2021},
booktitle = {Asia CCS},
pages = {672–686},
}

@inproceedings {272298,
author = {Nishat Koti and Mahak Pancholi and Arpita Patra and Ajith Suresh},
title = {{SWIFT}: Super-fast and Robust Privacy-Preserving Machine Learning},
booktitle = {{USENIX} Security},
year = {2021},
publisher = {{USENIX} Association},
}

@misc{hunt2018chiron,
      title={Chiron: Privacy-preserving Machine Learning as a Service}, 
      author={Tyler Hunt and Congzheng Song and Reza Shokri and Vitaly Shmatikov and Emmett Witchel},
      year={2018},
      eprint={1803.05961},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}


@misc{malekzadeh2021honestbutcurious,
      title={Honest-but-Curious Nets: Sensitive Attributes of Private Inputs can be Secretly Coded into the Entropy of Classifiers' Outputs}, 
      author={Mohammad Malekzadeh and Anastasia Borovykh and Deniz Gündüz},
      year={2021},
      eprint={2105.12049},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@InProceedings{pmlr-v130-vogel21a,
  title = 	 { Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints },
  author =       {Vogel, Robin and Bellet, Aur{\'e}lien and Cl{\'e}men{\c{c}}on, Stephan},
  booktitle = 	 {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {784--792},
  year = 	 {2021},
  editor = 	 {Banerjee, Arindam and Fukumizu, Kenji},
  volume = 	 {130},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--15 Apr},
}

@misc{chang2021privacy,
      title={On the Privacy Risks of Algorithmic Fairness}, 
      author={Hongyan Chang and Reza Shokri},
      year={2021},
      eprint={2011.03731},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{fedsurvey,
    title={Advances and Open Problems in Federated Learning},
    author={Peter Kairouz et al.},
    year={2019},
    eprint = {1912.04977}
}

@techreport{ec2019ethics,
  author = {High-Level Expert Group on AI},
  year={2019},
  title = {Ethics guidelines for trustworthy AI}
}

@article{stealingtime,
  author    = {Vasisht Duddu and
               Debasis Samanta and
               D. Vijay Rao and
               Valentina E. Balas},
  title     = {Stealing Neural Networks via Timing Side Channels},
  year      = {2018},
  eprint    = {1812.11720}
}

@misc{duddu2019quantifying,
    title={Quantifying (Hyper) Parameter Leakage in Machine Learning},
    author={Vasisht Duddu and D. Vijay Rao},
    year={2019},
    eprint={1910.14409}
}

@inproceedings{stealml,
author = {Tram\`{e}r, Florian and Zhang, Fan and Juels, Ari and Reiter, Michael K. and Ristenpart, Thomas},
title = {Stealing Machine Learning Models via Prediction APIs},
year = {2016},
booktitle = {USENIX Security},
pages = {601–618},
}


@misc{duddu2021gecko,
      title={GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning}, 
      author={Vasisht Duddu and Antoine Boutet and Virat Shejwalkar},
      year={2021},
      eprint={2010.00912},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

@misc{duddu2021quantifying,
      title={Quantifying Privacy Leakage in Graph Embedding}, 
      author={Vasisht Duddu and Antoine Boutet and Virat Shejwalkar},
      year={2021},
      eprint={2010.00906},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

@inproceedings{NEURIPS2020_6b8b8e3b,
 author = {Zhao, Han and Chi, Jianfeng and Tian, Yuan and Gordon, Geoffrey J},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {9485--9496},
 publisher = {Curran Associates, Inc.},
 title = {Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation},
 url = {https://proceedings.neurips.cc/paper/2020/file/6b8b8e3bd6ad94b985c1b1f1b7a94cb2-Paper.pdf},
 volume = {33},
 year = {2020}
}

@inproceedings{10.1145/3319535.3363201,
author = {Jia, Jinyuan and Salem, Ahmed and Backes, Michael and Zhang, Yang and Gong, Neil Zhenqiang},
title = {MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples},
year = {2019},
booktitle = {CCS},
pages = {259–274}
}


@INPROCEEDINGS{meminf,
author={R. {Shokri} and M. {Stronati} and C. {Song} and V. {Shmatikov}},
booktitle={SP},
year = {2017},
title={Membership Inference Attacks Against Machine Learning Models}
}

@inproceedings{fedinversion,
author = {Hitaj, Briland and Ateniese, Giuseppe and Perez-Cruz, Fernando},
title = {Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning},
year = {2017},
booktitle = {CCS},
pages = {603–618}
}





@INPROCEEDINGS{fedpriv2,
author={M. {Nasr} and R. {Shokri} and A. {Houmansadr}},
booktitle={SP},
year = {2019},
title={Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning},
pages={739-753}}


@inproceedings{fairprivatedata,
author = {Pujol, David and McKenna, Ryan and Kuppam, Satya and Hay, Michael and Machanavajjhala, Ashwin and Miklau, Gerome},
title = {Fair Decision Making Using Privacy-Protected Data},
year = {2020},
booktitle = {FAT*},
pages = {189–199},
}

@inproceedings{compatibility,
author = {Cummings, Rachel and Gupta, Varun and Kimpara, Dhamma and Morgenstern, Jamie},
title = {On the Compatibility of Privacy and Fairness},
year = {2019},
booktitle = {UMAP},
pages = {309–315}
}

@inproceedings{Song2020Overlearning,
title={Overlearning Reveals Sensitive Attributes},
author={Congzheng Song and Vitaly Shmatikov},
booktitle={International Conference on Learning Representations},
year={2020}
}

@ARTICLE{8515092,
author={S. A. {Osia} and A. {Taheri} and A. S. {Shamsabadi} and K. {Katevas} and H. {Haddadi} and H. R. {Rabiee}},
journal={IEEE Transactions on Knowledge and Data Engineering},
title={Deep Private-Feature Extraction},
year={2020},
volume={32},
number={1},
pages={54-66},}


@misc{removedispimpactdpsgd,
title={Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy},
author={Depeng Xu and Wei Du and Xintao Wu},
year={2020},
eprint = {2003.03699},
}

@article{incompatibility,
title={Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy},
author={Rastegarpanah, Bashir and Crovella, Mark and Gummadi, Krishna P},
eprint = {2005.09209},
year={2020}
}

@article{fairvrobust,
author    = {Hongyan Chang and Ta Duy Nguyen and Sasi Kumar Murakonda and Ehsan Kazemi and Reza Shokri},
title     = {On Adversarial Bias and the Robustness of Fair Machine Learning},
year      = {2020},
eprint = {2006.08669}
}

@article{dispvuln,
author    = {Mohammad Yaghini and  Bogdan Kulynych and Carmela Troncoso},
title     = {Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning},
year      = {2019},
eprint = {1906.00389}
}

@incollection{dpaccdisp,
title = {Differential Privacy Has Disparate Impact on Model Accuracy},
author = {Bagdasaryan, Eugene and Poursaeed, Omid and Shmatikov, Vitaly},
booktitle = {NIPS},
pages = {15479--15488},
year = {2019}}

@misc{dpmeminf,
title={Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack},
author={Bo Zhang and Ruotong Yu and Haipei Sun and Yanying Li and Jun Xu and Hui Wang},
year={2020},
eprint={2001.08855}
}









@article{advfair,
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  title     = {Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations},
  year      = {2017},
  eprint    = {1707.00075}
}

@article{propinf,
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}

@article{propinf2,
author = {Ateniese, Giuseppe and Mancini, Luigi V. and Spognardi, Angelo and Villani, Antonio and Vitali, Domenico and Felici, Giovanni},
title = {Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers},
year = {2015},
volume = {10},
number = {3},
journal = {Int. J. Secur. Netw.},
month = sep,
pages = {137–150}
}

@inproceedings{10.5555/3042817.3042973,
author = {Zemel, Richard and Wu, Yu and Swersky, Kevin and Pitassi, Toniann and Dwork, Cynthia},
title = {Learning Fair Representations},
year = {2013},
booktitle = {ICML}
}

@article{10.5555/3122009.3208010,
author = {Hamm, Jihun},
title = {Minimax Filter: Learning to Preserve Privacy from Inference Attacks},
year = {2017},
volume = {18},
number = {1},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {4704–4734}
}

@inproceedings{10.5555/3327546.3327583,
author = {Moyer, Daniel and Gao, Shuyang and Brekelmans, Rob and Steeg, Greg Ver and Galstyan, Aram},
title = {Invariant Representations without Adversarial Training},
year = {2018},
booktitle = {NIPS}
}

@inproceedings{10.5555/3294771.3294827,
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title = {Controllable Invariance through Adversarial Feature Learning},
year = {2017},
booktitle = {NIPS}
}

@InProceedings{pmlr-v80-madras18a,
  title = 	 {Learning Adversarially Fair and Transferable Representations},
  author =       {Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard},
  pages = 	 {3384--3393},
  year = 	 {2018},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
}

@inproceedings{censoringadv,
title     = "Censoring Representations with an Adversary",
author    = "Harrison Edwards and Amos Storkey",
year      = "2016",
booktitle = “ICLR”}






@inproceedings{propinf3,
author = {Ganju, Karan and Wang, Qi and Yang, Wei and Gunter, Carl A. and Borisov, Nikita},
title = {Property Inference Attacks on Fully Connected Neural Networks Using Permutation Invariant Representations},
year = {2018},
booktitle = {CCS},
pages = {619–633}
}

@inproceedings {whiteboxmeminf,
author = {Klas Leino and Matt Fredrikson},
title = {Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference},
booktitle = {USENIX Security},
year = {2020},
pages = {1605--1622}
}


@inproceedings{modelinv,
author = {Fredrikson, Matt and Jha, Somesh and Ristenpart, Thomas},
title = {Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures},
year = {2015},
booktitle = {CCS},
pages = {1322–1333}
}

@inproceedings{advtrain,
author = {Louppe, Gilles and Kagan, Michael and Cranmer, Kyle},
title = {Learning to Pivot with Adversarial Networks},
year = {2017},
booktitle = {NeurIPS},
pages = {982–991}
}

@inproceedings {attriguard,
author = {Jinyuan Jia and Neil Zhenqiang Gong},
title = {AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning},
booktitle = {USENIX Security},
year = {2018},
pages = {513--529},
}








@INPROCEEDINGS{fedinference,
author={L. {Melis} and C. {Song} and E. {De Cristofaro} and V. {Shmatikov}},
booktitle={SP},
title={Exploiting Unintended Feature Leakage in Collaborative Learning},
year={2019},
pages={691-706}}

@article{fairmetric,
author  = {Muhammad Bilal Zafar and Isabel Valera and Manuel Gomez-Rodriguez and Krishna P. Gummadi},
title   = {Fairness Constraints: A Flexible Approach for Fair Classification},
journal = {Journal of Machine Learning Research},
year    = {2019},
volume  = {20},
number  = {75},
pages   = {1-42}
}

@inproceedings{fairmetric2,
author = {Hardt, Moritz and Price, Eric and Srebro, Nathan},
title = {Equality of Opportunity in Supervised Learning},
year = {2016},
booktitle = {NIPS},
pages = {3323–3331}
}


@misc{yeom,
      title={Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting}, 
      author={Samuel Yeom and Irene Giacomelli and Matt Fredrikson and Somesh Jha},
      year={2018},
      booktitle = {CSF}
}

@inproceedings{Mahajan2020DoesLS,
  title={Does Learning Stable Features Provide Privacy Benefits for Machine Learning Models?},
  author={Divyat Mahajan, Shruti Tople, Amit Sharma},
  booktitle = {NeurIPS PPML Workshop},
  year={2020}
}

@msic{advrobtool,
    title={Adversarial robustness toolbox},
    howpublished={\url{https://adversarial-robustness-toolbox.org}},
    note = {Accessed: 2021-06-22}
}


@inproceedings{debiase,
author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
title = {Mitigating Unwanted Biases with Adversarial Learning},
year = {2018},
booktitle = {AIES},
pages = {335–340},
location = {New Orleans, LA, USA}
}

  
@article{preprocessing,
author = {Kamiran, Faisal and Calders, Toon},
year = {2011},
month = {10},
pages = {},
title = {Data Pre-Processing Techniques for Classification without Discrimination},
volume = {33},
journal = {Knowledge and Information Systems},
doi = {10.1007/s10115-011-0463-8}
}


@InProceedings{reductions,
  title = 	 {A Reductions Approach to Fair Classification},
  author =       {Agarwal, Alekh and Beygelzimer, Alina and Dudik, Miroslav and Langford, John and Wallach, Hanna},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {60--69},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/agarwal18a/agarwal18a.pdf},
  url = 	 {http://proceedings.mlr.press/v80/agarwal18a.html},
  abstract = 	 {We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints. We introduce two reductions that work for any representation of the cost-sensitive classifier and compare favorably to prior baselines on a variety of data sets, while overcoming several of their disadvantages.}
}
@article{recomender,
   title={Data Poisoning Attacks to Deep Learning Based Recommender Systems},
   ISBN={1891562665},
   url={http://dx.doi.org/10.14722/ndss.2021.24525},
   DOI={10.14722/ndss.2021.24525},
   journal={Proceedings 2021 Network and Distributed System Security Symposium},
   publisher={Internet Society},
   author={Huang, Hai and Mu, Jiaming and Gong, Neil Zhenqiang and Li, Qi and Liu, Bin and Xu, Mingwei},
   year={2021}
}


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	author={Reyes-Ortiz, J. L.},
	year={2015},
	publisher={Springer}
}

% Encoding: UTF-8

@inproceedings{DBLP:conf/srds/ContiuVPPFR19,
  author    = {Stefan Contiu and
               S{\'{e}}bastien Vaucher and
               Rafael Pires and
               Marcelo Pasin and
               Pascal Felber and
               Laurent R{\'{e}}veill{\`{e}}re},
  title     = {Anonymous and Confidential File Sharing over Untrusted Clouds},
  booktitle = {SRDS},
  pages     = {21--31},
  year      = {2019},
}

@article{10.1504/IJSN.2015.071829,
author = {Ateniese, Giuseppe and Mancini, Luigi V. and Spognardi, Angelo and Villani, Antonio and Vitali, Domenico and Felici, Giovanni},
title = {Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers},
year = {2015},
volume = {10},
number = {3},
journal = {Int. J. Secur. Netw.},
month = sep,
pages = {137–150},
numpages = {14}
}



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      year={2018},
      journal={arXiv:1806.01246},
}

@inproceedings{DBLP:conf/middleware/SilvaMCNRR19,
  author    = {Simon Da Silva and
               Sonia Ben Mokhtar and
               Stefan Contiu and
               Daniel N{\'{e}}gru and
               Laurent R{\'{e}}veill{\`{e}}re and
               Etienne Rivi{\`{e}}re},
  title     = {PrivaTube: Privacy-Preserving Edge-Assisted Video Streaming},
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  pages     = {189--201},
  year      = {2019},
}

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}

@article{shin2018privacy,
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}

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  year={2015},
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@article{gunes2014shilling,
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@inproceedings{muhammad2020fedfast,
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@inproceedings{garcin2014offline,
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@inproceedings{ge2010beyond,
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@article{bobadilla2013recommender,
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@article{MovieLens,
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@incollection{burke2015robust,
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