5 citations · 6 across the 6 of their papers we have counts for
7 papers
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 Persists under Unlimited Synthetic Data Release
Clément Pierquin, Aurélien Bellet, Marc Tommasi +1
We study privacy amplification by synthetic data release, a phenomenon in which differential privacy guarantees are improved by releasing only synthetic data rather than the privat…
Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems
Natalia Tomashenko, Emmanuel Vincent, Marc Tommasi
The temporal dynamics of speech, encompassing variations in rhythm, intonation, and speaking rate, contain important and unique information about speaker identity. This paper propo…
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…
Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization
Natalia Tomashenko, Emmanuel Vincent, Marc Tommasi
In this paper, we investigate the impact of speech temporal dynamics in application to automatic speaker verification and speaker voice anonymization tasks. We propose several metr…
Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
Clément Pierquin, Aurélien Bellet, Marc Tommasi +1
Pufferfish privacy is a flexible generalization of differential privacy that allows to model arbitrary secrets and adversary's prior knowledge about the data. Unfortunately, design…