5 citations · 6 across the 6 of their papers we have counts for
3 papers · 1 filter
Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
Jean Dufraiche, Paul Mangold, Michaël Perrot +1
Releasing data once and for all under noninteractive Local Differential Privacy (LDP) enables complete data reusability, but the resulting noise may create bias in subsequent analy…
Privacy Amplification Through Synthetic Data: Insights from Linear Regression
Clément Pierquin, Aurélien Bellet, Marc Tommasi +1
Synthetic data inherits the differential privacy guarantees of the model used to generate it. Additionally, synthetic data may benefit from privacy amplification when the generativ…
TAMIS: Tailored Membership Inference Attacks on Synthetic Data
Paul Andrey, Batiste Le Bars, Marc Tommasi
Membership Inference Attacks (MIA) enable to empirically assess the privacy of a machine learning algorithm. In this paper, we propose TAMIS, a novel MIA against differentially-pri…