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authorJan Aalmoes <jan.aalmoes@inria.fr>2024-09-21 16:33:51 +0200
committerJan Aalmoes <jan.aalmoes@inria.fr>2024-09-21 16:33:51 +0200
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+\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.