161 citations · 387 across the 12 of their papers we have counts for
12 papers
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…
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
Chulin Xie, Zhong Cao, Yunhui Long +3
Recent advances in machine learning have enabled its wide application in different domains, and one of the most exciting applications is autonomous vehicles (AVs), which have encou…
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…
SecretGen: Privacy Recovery on Pre-Trained Models via Distribution Discrimination
Zhuowen Yuan, Fan Wu, Yunhui Long +2
Transfer learning through the use of pre-trained models has become a growing trend for the machine learning community. Consequently, numerous pre-trained models are released online…
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis
Fan Wu, Yunhui Long, Ce Zhang +1
Graph structured data have enabled several successful applications such as recommendation systems and traffic prediction, given the rich node features and edges information. Howeve…
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
Boxin Wang, Fan Wu, Yunhui Long +3
Recent success of deep neural networks (DNNs) hinges on the availability of large-scale dataset; however, training on such dataset often poses privacy risks for sensitive training…