13 citations · 35 across the 7 of their papers we have counts for
3 papers · 1 filter
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning
Zebang Shen, Jiayuan Ye, Anmin Kang +2
Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy. Mitigating the risk of th…
Leave-one-out Distinguishability in Machine Learning
Jiayuan Ye, Anastasia Borovykh, Soufiane Hayou +1
We introduce an analytical framework to quantify the changes in a machine learning algorithm's output distribution following the inclusion of a few data points in its training set,…
Enhanced Membership Inference Attacks against Machine Learning Models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda +2
How much does a machine learning algorithm leak about its training data, and why? Membership inference attacks are used as an auditing tool to quantify this leakage. In this paper,…