1.4k citations · 1.6k across the 12 of their papers we have counts for
12 papers
Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption
Jian Yang, Yuan Tong, Qinbin Li +2
Preserving data privacy is an important topic in structural data management and data mining. However, the issue of privacy leakage in distributed causal structure learning is a per…
Exploiting Label Skews in Federated Learning with Model Concatenation
Yiqun Diao, Qinbin Li, Bingsheng He
Federated Learning (FL) has emerged as a promising solution to perform deep learning on different data owners without exchanging raw data. However, non-IID data has been a key chal…
SoK: Privacy-Preserving Data Synthesis
Yuzheng Hu, Fan Wu, Qinbin Li +7
As the prevalence of data analysis grows, safeguarding data privacy has become a paramount concern. Consequently, there has been an upsurge in the development of mechanisms aimed a…
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
Yiqun Diao, Yutong Yang, Qinbin Li +2
How to get insights from relational data streams in a timely manner is a hot research topic. Data streams can present unique challenges, such as distribution drifts, outliers, emer…
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
Chulin Xie, Yunhui Long, Pin-Yu Chen +4
Federated learning (FL) provides an efficient paradigm to jointly train a global model leveraging data from distributed users. As local training data comes from different users who…
Practical Vertical Federated Learning with Unsupervised Representation Learning
Zhaomin Wu, Qinbin Li, Bingsheng He
As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications. Federated learning emerges as a new learning pa…