5 citations · 35 across the 26 of their papers we have counts for
26 papers
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…
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…
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…
On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation
Yucong Liu, Chi-Hua Wang, Guang Cheng
Devising procedures for auditing generative model privacy-utility tradeoff is an important yet unresolved problem in practice. Existing works concentrates on investigating the priv…
Always Valid Risk Monitoring for Online Matrix Completion
Chi-Hua Wang, Wenjie Li
Always-valid concentration inequalities are increasingly used as performance measures for online statistical learning, notably in the learning of generative models and supervised l…
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing
Po-Yi Liu, Chi-Hua Wang, Henghsiu Tsai
This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts. The agents run p…
Dynamic Matching Bandit For Two-Sided Online Markets
Yuantong Li, Chi-hua Wang, Guang Cheng +1
Two-sided online matching platforms are employed in various markets. However, agents' preferences in the current market are usually implicit and unknown, thus needing to be learned…
Federated Online Sparse Decision Making
Chi-Hua Wang, Wenjie Li, Guang Cheng +1
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and…
Residual Bootstrap Exploration for Stochastic Linear Bandit
Shuang Wu, Chi-Hua Wang, Yuantong Li +1
We propose a new bootstrap-based online algorithm for stochastic linear bandit problems. The key idea is to adopt residual bootstrap exploration, in which the agent estimates the n…
Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization
Wenjie Li, Chi-Hua Wang, Guang Cheng +1
In this paper, we make the key delineation on the roles of resolution and statistical uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more ge…
Online Forgetting Process for Linear Regression Models
Yuantong Li, Chi-hua Wang, Guang Cheng
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of ti…
Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing
Chi-Hua Wang, Zhanyu Wang, Will Wei Sun +1
Devising dynamic pricing policy with always valid online statistical learning procedure is an important and as yet unresolved problem. Most existing dynamic pricing policy, which f…