22 citations · 49 across the 5 of their papers we have counts for
7 papers
How to Combine Membership-Inference Attacks on Multiple Updated Models
Matthew Jagielski, Stanley Wu, Alina Oprea +2
A large body of research has shown that machine learning models are vulnerable to membership inference (MI) attacks that violate the privacy of the participants in the training dat…
Toward Training at ImageNet Scale with Differential Privacy
Alexey Kurakin, Shuang Song, Steve Chien +3
Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the tr…
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…
Pythia: Grammar-Based Fuzzing of REST APIs with Coverage-guided Feedback and Learning-based Mutations
Vaggelis Atlidakis, Roxana Geambasu, Patrice Godefroid +2
This paper introduces Pythia, the first fuzzer that augments grammar-based fuzzing with coverage-guided feedback and a learning-based mutation strategy for stateful REST API fuzzin…
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
Mathias Lecuyer, Riley Spahn, Kiran Vodrahalli +2
Companies increasingly expose machine learning (ML) models trained over sensitive user data to untrusted domains, such as end-user devices and wide-access model stores. We present…
Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu +2
Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed…