13 citations · 35 across the 7 of their papers we have counts for
8 papers
Instance-Optimality for Private KL Distribution Estimation
Jiayuan Ye, Vitaly Feldman, Kunal Talwar
We study the fundamental problem of estimating an unknown discrete distribution over symbols, given i.i.d. samples from the distribution. We are interested in minimizin…
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…
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,…
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
Jiayuan Ye, Reza Shokri
Prior work on differential privacy analysis of randomized SGD algorithms relies on composition theorems, where the implicit (unrealistic) assumption is that the internal state of t…
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,…
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
Rishav Chourasia, Jiayuan Ye, Reza Shokri
What is the information leakage of an iterative randomized learning algorithm about its training data, when the internal state of the algorithm is \emph{private}? How much is the c…