10.1k citations · 21.9k across the 280 of their papers we have counts for
15 papers · 1 filter
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lingjuan Lyu, Han Yu, Xingjun Ma +5
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (…
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Tianqing Zhu, Dayong Ye, Wei Wang +2
Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violation…
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy
Lichao Sun, Jianwei Qian, Xun Chen
Train machine learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that coll…
Target Privacy Preserving for Social Networks
Zhongyuan Jiang, Lichao Sun, Philip S. Yu +3
In this paper, we incorporate the realistic scenario of key protection into link privacy preserving and propose the target-link privacy preserving (TPP) model: target links referre…
Heterogeneous Graph Matching Networks
Shen Wang, Zhengzhang Chen, Xiao Yu +7
Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone t…
Not Just Cloud Privacy: Protecting Client Privacy in Teacher-Student Learning
Lichao Sun, Ji Wang, Philip S. Yu +1
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One recent popular approach to study these…