1 citations · 1 across the 4 of their papers we have counts for
5 papers
Struct-Bench: A Benchmark for Differentially Private Structured Text Generation
Shuaiqi Wang, Vikas Raunak, Arturs Backurs +7
Differentially private (DP) synthetic data generation is a promising technique for utilizing private datasets that otherwise cannot be exposed for model training or other analytics…
Evaluating Selective Encryption Against Gradient Inversion Attacks
Jiajun Gu, Yuhang Yao, Shuaiqi Wang +1
Gradient inversion attacks pose significant privacy threats to distributed training frameworks such as federated learning, enabling malicious parties to reconstruct sensitive local…
Data Distribution Valuation
Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo +2
Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a…
Inferentially-Private Private Information
Shuaiqi Wang, Shuran Zheng, Zinan Lin +2
Information disclosure can compromise privacy when revealed information is correlated with private information. We consider the notion of inferential privacy, which measures privac…
Guarding Multiple Secrets: Enhanced Summary Statistic Privacy for Data Sharing
Shuaiqi Wang, Rongzhe Wei, Mohsen Ghassemi +2
Data sharing enables critical advances in many research areas and business applications, but it may lead to inadvertent disclosure of sensitive summary statistics (e.g., means or q…