2 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting
Joshua Ward, Chi-Hua Wang, Guang Cheng
Synthetic tabular data has gained attention for enabling privacy-preserving data sharing. While substantial progress has been made in single-table synthetic generation where data a…
When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation
Joshua Ward, Bochao Gu, Chi-Hua Wang +1
Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice, two primary approaches have emerged f…
Privacy Auditing Synthetic Data Release through Local Likelihood Attacks
Joshua Ward, Chi-Hua Wang, Guang Cheng
Auditing the privacy leakage of synthetic data is an important but unresolved problem. Existing privacy auditing frameworks for synthetic data rely on heuristics and unrealistic as…
Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models
Joshua Ward, Chi-Hua Wang, Guang Cheng
The promise of tabular generative models is to produce realistic synthetic data that can be shared and safely used without dangerous leakage of information from the training set. I…