25 citations · 140 across the 18 of their papers we have counts for
12 papers · 1 filter
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
Jun Zhou, Longfei Zheng, Chaochao Chen +6
Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has b…
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
Yilun Lin, Chaochao Chen, Cen Chen +1
Federated learning (FL) has attracted increasing attention in recent years. As a privacy-preserving collaborative learning paradigm, it enables a broader range of applications, esp…
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
Chaochao Chen, Jamie Cui, Guanfeng Liu +2
Knowledge Graph (KG) has attracted more and more companies' attention for its ability to connect different types of data in meaningful ways and support rich data services. However,…
A Theoretical Perspective on Differentially Private Federated Multi-task Learning
Huiwen Wu, Cen Chen, Li Wang
In the era of big data, the need to expand the amount of data through data sharing to improve model performance has become increasingly compelling. As a result, effective collabora…
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy
Bin Zhang, Cen Chen, Li Wang
The success of machine learning algorithms often relies on a large amount of high-quality data to train well-performed models. However, data is a valuable resource and are always h…
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning
Cen Chen, Bingzhe Wu, Minghui Qiu +2
Transfer learning is widely used for transferring knowledge from a source domain to the target domain where the labeled data is scarce. Recently, deep transfer learning has achieve…