66 citations · 76 across the 6 of their papers we have counts for
6 papers
Segmented Private Data Aggregation in the Multi-message Shuffle Model
Shaowei Wang, Hongqiao Chen, Sufen Zeng +8
The shuffle model of differential privacy (DP) offers compelling privacy-utility trade-offs in decentralized settings (e.g., internet of things, mobile edge networks). Particularly…
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened
Shaowei Wang, Yun Peng, Jin Li +5
The shuffle model of differential privacy provides promising privacy-utility balances in decentralized, privacy-preserving data analysis. However, the current analyses of privacy a…
Fine-grained Private Knowledge Distillation
Yuntong Li, Shaowei Wang, Yingying Wang +4
Knowledge distillation has emerged as a scalable and effective way for privacy-preserving machine learning. One remaining drawback is that it consumes privacy in a model-level (i.e…
Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. Indistinguishability
Shaowei Wang, Jiachun Du, Wei Yang +5
Voting plays a central role in bringing crowd wisdom to collective decision making, meanwhile data privacy has been a common ethical/legal issue in eliciting preferences from indiv…
Personalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing
Shaowei Wang, Liusheng Huang, Pengzhan Wang +2
Ensemble learning has been widely employed by mobile applications, ranging from environmental sensing to activity recognitions. One of the fundamental issue in ensemble learning is…
Mutual Information Optimally Local Private Discrete Distribution Estimation
Shaowei Wang, Liusheng Huang, Pengzhan Wang +5
Consider statistical learning (e.g. discrete distribution estimation) with local -differential privacy, which preserves each data provider's privacy locally, we aim to optimize…