25 citations · 140 across the 18 of their papers we have counts for
18 papers
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
Jamie Cui, Chaochao Chen, Lingjuan Lyu +2
Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that a…
Backdoor Defense with Machine Unlearning
Yang Liu, Mingyuan Fan, Cen Chen +4
Backdoor injection attack is an emerging threat to the security of neural networks, however, there still exist limited effective defense methods against the attack. In this paper,…
Practical and Light-weight Secure Aggregation for Federated Submodel Learning
Jamie Cui, Cen Chen, Tiandi Ye +1
Recently, Niu, et. al. introduced a new variant of Federated Learning (FL), called Federated Submodel Learning (FSL). Different from traditional FL, each client locally trains the…
Privacy Threats Analysis to Secure Federated Learning
Yuchen Li, Yifan Bao, Liyao Xiang +4
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties. It is renowned for preserving privacy as the data never le…
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…