5 citations · 35 across the 26 of their papers we have counts for
6 papers · 1 filter
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…
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…
Ensembling Membership Inference Attacks Against Tabular Generative Models
Joshua Ward, Yuxuan Yang, Chi-Hua Wang +1
Membership Inference Attacks (MIAs) have emerged as a principled framework for auditing the privacy of synthetic data generated by tabular generative models, where many diverse met…
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…
Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering
Tung Sum Thomas Kwok, Zeyong Zhang, Chi-Hua Wang +1
Tabular data synthesis for supervised learning ('SL') model training is gaining popularity in industries such as healthcare, finance, and retail. Despite the progress made in tabul…
GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
Tung Sum Thomas Kwok, Chi-Hua Wang, Guang Cheng
Tabular data synthesis involves not only multi-table synthesis but also generating multi-modal data (e.g., strings and categories), which enables diverse knowledge synthesis. Howev…