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author | Jan Aalmoes <jan.aalmoes@inria.fr> | 2024-09-21 16:33:51 +0200 |
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committer | Jan Aalmoes <jan.aalmoes@inria.fr> | 2024-09-21 16:33:51 +0200 |
commit | b8504c330be30ccf771d6745a34f395a83395ea5 (patch) | |
tree | 9bbd9285d530381e3e743266fbf62be35df3a7c8 /synthetic/bck/introduction.tex | |
parent | 00ec61946ddf3a7c2abf7d7e0730fc8e21b50f37 (diff) | |
parent | 06c724f61e746772dc46aaf7e11c96abc1a49dd1 (diff) |
merge with brouillon
Diffstat (limited to 'synthetic/bck/introduction.tex')
-rw-r--r-- | synthetic/bck/introduction.tex | 9 |
1 files changed, 9 insertions, 0 deletions
diff --git a/synthetic/bck/introduction.tex b/synthetic/bck/introduction.tex new file mode 100644 index 0000000..e8969c2 --- /dev/null +++ b/synthetic/bck/introduction.tex @@ -0,0 +1,9 @@ +\subsection{Research questions} +\label{sec:question} + +%\textbf{What is the impact of using synthetic data instead of real data on users' privacy when training machine learning models?} +\textbf{How does using synthetic data instead of real data affect users' privacy in the context of training machine learning models?} + +User's privacy and neural network clash at two levels: membership inference and attribute inference. +Membership inference refers to the possibility of infering weather or not a data record belongs to the training data. +Attribute inference refers to how a trained model can be leveraged to infer a sensitive attribute such as the race or the gender. |