523 citations · 1.4k across the 79 of their papers we have counts for
9 papers · 1 filter
Privacy Foundations for Multi-Institutional Scientific Artificial Intelligence
Olivera Kotevska, Sumit Jha, Aur'elien Bellet +6
Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, univ…
Privacy Auditing with Zero (0) Training Run
Tudor Cebere, Mathieu Even, Linus Bleistein +1
Privacy auditing provides empirical lower bounds on the differential privacy parameters of learning algorithms. Existing methods, however, require interventional access to the trai…
Privacy in Theory, Bugs in Practice: Grey-Box Auditing of Differential Privacy Libraries
Tudor Cebere, David Erb, Damien Desfontaines +2
Differential privacy (DP) implementations are notoriously prone to errors, with subtle bugs frequently invalidating theoretical guarantees. Existing verification methods are often…
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…
Nebula: Efficient, Private and Accurate Histogram Estimation
Ali Shahin Shamsabadi, Peter Snyder, Ralph Giles +2
We present \textit{Nebula}, a system for differentially private histogram estimation on data distributed among clients. \textit{Nebula} allows clients to independently decide wheth…
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…