4 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.CR2023★ 1 cited
Bounded and Unbiased Composite Differential Privacy
Kai Zhang, Yanjun Zhang, Ruoxi Sun +5
The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between any two neighboring databases. However, tradi…
cs.LG2021★ 3 cited
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning
Yanjun Zhang, Guangdong Bai, Xue Li +2
Federated learning enables multiple participants to collaboratively train a model without aggregating the training data. Although the training data are kept within each participant…
cs.CR2020★ 4 cited
PrivColl: Practical Privacy-Preserving Collaborative Machine Learning
Yanjun Zhang, Guangdong Bai, Xue Li +3
Collaborative learning enables two or more participants, each with their own training dataset, to collaboratively learn a joint model. It is desirable that the collaboration should…