3 citations · 13 across the 12 of their papers we have counts for
12 papers
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…
DEREC-SIMPRO: unlock Language Model benefits to advance Synthesis in Data Clean Room
Tung Sum Thomas Kwok, Chi-hua Wang, Guang Cheng
Data collaboration via Data Clean Room offers value but raises privacy concerns, which can be addressed through synthetic data and multi-table synthesizers. Common multi-table synt…
Data Deletion for Linear Regression with Noisy SGD
Zhangjie Xia, Chi-Hua Wang, Guang Cheng
In the current era of big data and machine learning, it's essential to find ways to shrink the size of training dataset while preserving the training performance to improve efficie…
Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data
Yu Xia, Chi-Hua Wang, Joshua Mabry +1
The evaluation of synthetic data generation is crucial, especially in the retail sector where data accuracy is paramount. This paper introduces a comprehensive framework for assess…
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…
BadGD: A unified data-centric framework to identify gradient descent vulnerabilities
Chi-Hua Wang, Guang Cheng
We present BadGD, a unified theoretical framework that exposes the vulnerabilities of gradient descent algorithms through strategic backdoor attacks. Backdoor attacks involve embed…
Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data
Lan Tao, Shirong Xu, Chi-Hua Wang +2
With the proliferation of generative AI and the increasing volume of generative data (also called as synthetic data), assessing the fidelity of generative data has become a critica…
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…
Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective
Din-Yin Hsieh, Chi-Hua Wang, Guang Cheng
Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges. This pape…