From bf5b05a84e877391fddd1b0a0b752f71ec05e901 Mon Sep 17 00:00:00 2001
From: Jan Aalmoes <jan.aalmoes@inria.fr>
Date: Wed, 11 Sep 2024 00:10:50 +0200
Subject: Preuve existe f pas cca equivalant exists f BA pas randomguess

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 synthetic/bck/introduction.tex | 9 +++++++++
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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.
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