4 papers
DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy
Erchi Wang, Pengrun Huang, Eli Chien +4
Differential privacy (DP) has a wide range of applications for protecting data privacy, but designing and verifying DP algorithms requires expert-level reasoning, creating a high b…
Purifying Approximate Differential Privacy with Randomized Post-processing
Yingyu Lin, Erchi Wang, Yi-An Ma +1
We propose a framework to convert -approximate Differential Privacy (DP) mechanisms into -pure DP mechanisms under certain conditions, a proce…
Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
Ruihan Wu, Erchi Wang, Zhiyuan Zhang +1
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving documents from an external corpus at inference time. When this corpus contains sensitive in…
Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning
Erchi Wang, Yuqing Zhu, Yu-Xiang Wang
This paper studies the problem of differentially private empirical risk minimization (DP-ERM) for binary linear classification. We obtain an efficient -DP algorit…