22 citations · 74 across the 8 of their papers we have counts for
8 papers · 1 filter
Debugging Differential Privacy: A Case Study for Privacy Auditing
Florian Tramer, Andreas Terzis, Thomas Steinke +3
Differential Privacy can provide provable privacy guarantees for training data in machine learning. However, the presence of proofs does not preclude the presence of errors. Inspir…
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes
Peter Grünwald, Thomas Steinke, Lydia Zakynthinou
We give a novel, unified derivation of conditional PAC-Bayesian and mutual information (MI) generalization bounds. We derive conditional MI bounds as an instance, with special choi…
Leveraging Public Data for Practical Private Query Release
Terrance Liu, Giuseppe Vietri, Thomas Steinke +2
In many statistical problems, incorporating priors can significantly improve performance. However, the use of prior knowledge in differentially private query release has remained u…
New Oracle-Efficient Algorithms for Private Synthetic Data Release
Giuseppe Vietri, Grace Tian, Mark Bun +2
We present three new algorithms for constructing differentially private synthetic data---a sanitized version of a sensitive dataset that approximately preserves the answers to a la…
Reasoning About Generalization via Conditional Mutual Information
Thomas Steinke, Lydia Zakynthinou
We provide an information-theoretic framework for studying the generalization properties of machine learning algorithms. Our framework ties together existing approaches, including…
A Hybrid Approach to Privacy-Preserving Federated Learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar +4
Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality durin…