791 citations · 1.8k across the 42 of their papers we have counts for
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stat.ML2015★ 96 cited
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu-Xiang Wang, Stephen E. Fienberg, Alex Smola
We consider the problem of Bayesian learning on sensitive datasets and present two simple but somewhat surprising results that connect Bayesian learning to "differential privacy:,…
stat.ML2015★ 76 cited
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg
While machine learning has proven to be a powerful data-driven solution to many real-life problems, its use in sensitive domains has been limited due to privacy concerns. A popular…