69 citations · 101 across the 8 of their papers we have counts for
8 papers
RaMark: Radioactive Watermarking for Generated Tabular Data
Xin Che, Lingyang Chu, Qiqi Zhang +3
Recent advances in generative modeling have made generated tabular data a practical solution for privacy-sensitive data sharing, where watermarking enables ownership verification.…
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining
Xidong Wu, Zhengmian Hu, Jian Pei +1
Multi-party collaborative training, such as distributed learning and federated learning, is used to address the big data challenges. However, traditional multi-party collaborative…
DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization
Honglu Jiang, Haotian Yu, Xiuzhen Cheng +3
A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted…
Knowledge-Injected Federated Learning
Zhenan Fan, Zirui Zhou, Jian Pei +4
Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold…
On Shapley Value in Data Assemblage Under Independent Utility
Xuan Luo, Jian Pei, Zicun Cong +1
In many applications, an organization may want to acquire data from many data owners. Data marketplaces allow data owners to produce data assemblage needed by data buyers through c…
Revealing Unfair Models by Mining Interpretable Evidence
Mohit Bajaj, Lingyang Chu, Vittorio Romaniello +5
The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical…
Fair and efficient contribution valuation for vertical federated learning
Zhenan Fan, Huang Fang, Xinglu Wang +4
Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data. Vertical federated learning, also known as…
Improving Fairness for Data Valuation in Horizontal Federated Learning
Zhenan Fan, Huang Fang, Zirui Zhou +4
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federat…