2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CR2025
Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
Joshua Ward, Xiaofeng Lin, Chi-Hua Wang +1
Tabular Generative Models are often argued to preserve privacy by creating synthetic datasets that resemble training data. However, auditing their empirical privacy remains challen…
cs.LG2024★ 2 cited
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…
cs.LG2024★ 1 cited
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
Yinan Cheng, Chi-Hua Wang, Vamsi K. Potluru +2
Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance. Existing studies focused on the utility of a single f…