9 citations · 11 across the 7 of their papers we have counts for
7 papers
Differentially Private Continual Release with Relative Error
Bo Li, Wei Wang, Peng Ye
This work investigates several fundamental tasks, including , , , and , in the continual release model und…
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…
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…
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learning
Peng Ye, Zhifeng Jiang, Wei Wang +2
Federated learning (FL) has increasingly been deployed, in its vertical form, among organizations to facilitate secure collaborative training over siloed data. In vertical FL (VFL)…