2 citations · 2 across the 7 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.LG2024
The Limits of Differential Privacy in Online Learning
Bo Li, Wei Wang, Peng Ye
Differential privacy (DP) is a formal notion that restricts the privacy leakage of an algorithm when running on sensitive data, in which privacy-utility trade-off is one of the cen…
cs.LG2024
Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy
Bo Li, Wei Wang, Peng Ye
Machine Learning has made remarkable progress in a wide range of fields. In many scenarios, learning is performed on datasets involving sensitive information, in which privacy prot…
cs.CR2024★ 2 cited
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning
Zhifeng Jiang, Peng Ye, Shiqi He +3
In Federated Learning (FL), common privacy-enhancing techniques, such as secure aggregation and distributed differential privacy, rely on the critical assumption of an honest major…