no code implementations • 4 Jul 2023 • Florent Guépin, Matthieu Meeus, Ana-Maria Cretu, Yves-Alexandre de Montjoye
While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset.
no code implementations • 17 Jun 2023 • Matthieu Meeus, Florent Guépin, Ana-Maria Cretu, Yves-Alexandre de Montjoye
The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective.
no code implementations • 16 Dec 2021 • Ana-Maria Creţu, Florent Guépin, Yves-Alexandre de Montjoye
Second, we propose a model-based attack, showing how an attacker can exploit black-box access to the model to infer the correlations using shadow models trained on synthetic datasets.