8 citations · 17 across the 9 of their papers we have counts for
4 papers · 1 filter
Cookie Monster: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems
Pierre Tholoniat, Kelly Kostopoulou, Peter McNeely +6
With the impending removal of third-party cookies from major browsers and the introduction of new privacy-preserving advertising APIs, the research community has a timely opportuni…
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
Mishaal Kazmi, Hadrien Lautraite, Alireza Akbari +5
We present PANORAMIA, a privacy leakage measurement framework for machine learning models that relies on membership inference attacks using generated data as non-members. By relyin…
DPack: Efficiency-Oriented Privacy Budget Scheduling
Pierre Tholoniat, Kelly Kostopoulou, Mosharaf Chowdhury +4
Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can…
Privacy Budget Scheduling
Tao Luo, Mingen Pan, Pierre Tholoniat +3
Machine learning (ML) models trained on personal data have been shown to leak information about users. Differential privacy (DP) enables model training with a guaranteed bound on t…