4 papers
Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings
Bo Li, Wei Wang, Peng Ye
We revisit the problem of private online learning, in which a learner receives a sequence of data points and has to respond at each time-step a hypothesis. It is required that…
Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity
Bo Li, Wei Wang, Peng Ye
The realizable-to-agnostic transformation (Beimel et al., 2015; Alon et al., 2020) provides a general mechanism to convert a private learner in the realizable setting (where the ex…
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…
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…