5 papers · 1 filter
Reliable Estimation of Differential Privacy under Selection Bias
Valentin Dorseuil, Meenatchi Sundaram Muthu Selva Annamalai, Jamal Atif +2
Statistical privacy audits are essential for verifying the theoretical privacy guarantees of differentially private (DP) mechanisms. Auditors typically use Membership Inference Att…
Counterfactual Certification for Sequential Unlearning with Long-Run Performance Guarantees
Hanna Benarroch, Jamal Atif, Olivier Cappé
Certified machine unlearning ensures the proximity between the unlearned model and a model retrained without the data to be forgotten. Extending this framework to sequential deleti…
Assessing Per-Sample Membership Inference Vulnerability without Retraining
Valentin Dorseuil, Jamal Atif, Olivier Cappé
Recent work in the privacy literature shows that sample-targeted membership inference attacks (MIAs) significantly outperform untargeted approaches by a wide margin. Motivated by t…
Certified Per-Instance Unlearning Using Individual Sensitivity Bounds
Hanna Benarroch, Jamal Atif, Olivier Cappé
Certified machine unlearning can be achieved through noise injection, with differential privacy guarantees obtained by calibrating the noise to worst-case sensitivity. Yet, the inf…
Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning
Constant Bourdrez, Alexandre Vérine, Olivier Cappé
Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the sampling algorithm itself (noise sch…