90 citations · 331 across the 28 of their papers we have counts for
5 papers · 1 filter
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Ahmed Salem, Giovanni Cherubin, David Evans +5
Deploying machine learning models in production may allow adversaries to infer sensitive information about training data. There is a vast literature analyzing different types of in…
Membership Inference Attacks and Generalization: A Causal Perspective
Teodora Baluta, Shiqi Shen, S. Hitarth +2
Membership inference (MI) attacks highlight a privacy weakness in present stochastic training methods for neural networks. It is not well understood, however, why they arise. Are t…
Invariant Aggregator for Defending against Federated Backdoor Attacks
Xiaoyang Wang, Dimitrios Dimitriadis, Sanmi Koyejo +1
Federated learning enables training high-utility models across several clients without directly sharing their private data. As a downside, the federated setting makes the model vul…
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…
Bayesian Estimation of Differential Privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople +6
Algorithms such as Differentially Private SGD enable training machine learning models with formal privacy guarantees. However, there is a discrepancy between the protection that su…