9 citations · 12 across the 10 of their papers we have counts for
4 papers · 2 filters
DiPPS: Differentially Private Propensity Scores for Bias Correction
Liangwei Chen, Valentin Hartmann, Robert West
In surveys, it is typically up to the individuals to decide if they want to participate or not, which leads to participation bias: the individuals willing to share their data might…
On the Choice of Databases in Differential Privacy Composition
Valentin Hartmann, Vincent Bindschaedler, Robert West
Differential privacy (DP) is a widely applied paradigm for releasing data while maintaining user privacy. Its success is to a large part due to its composition property that guaran…
Distribution inference risks: Identifying and mitigating sources of leakage
Valentin Hartmann, Léo Meynent, Maxime Peyrard +3
A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inferen…
Privacy accounting conomics: Improving differential privacy composition via a posteriori bounds
Valentin Hartmann, Vincent Bindschaedler, Alexander Bentkamp +1
Differential privacy (DP) is a widely used notion for reasoning about privacy when publishing aggregate data. In this paper, we observe that certain DP mechanisms are amenable to a…