83 citations · 86 across the 2 of their papers we have counts for
6 papers
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via -Differential Privacy
Chendi Wang, Buxin Su, Jiayuan Ye +2
Differentially private (DP) machine learning algorithms incur many sources of randomness, such as random initialization, random batch subsampling, and shuffling. However, such rand…
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
Jiayuan Ye, Zhenyu Zhu, Fanghui Liu +2
We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data. Specifically, w…
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…
Bias Propagation in Federated Learning
Hongyan Chang, Reza Shokri
We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive…
Defeating Image Obfuscation with Deep Learning
Richard McPherson, Reza Shokri, Vitaly Shmatikov
We demonstrate that modern image recognition methods based on artificial neural networks can recover hidden information from images protected by various forms of obfuscation. The o…
Prolonging the Hide-and-Seek Game: Optimal Trajectory Privacy for Location-Based Services
George Theodorakopoulos, Reza Shokri, Carmela Troncoso +2
Human mobility is highly predictable. Individuals tend to only visit a few locations with high frequency, and to move among them in a certain sequence reflecting their habits and d…