161 citations · 387 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
Yunhui Long, Boxin Wang, Zhuolin Yang +4
Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this w…