1 citations · 2 across the 7 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.CR2024
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
Zhibo Wang, Zhiwei Chang, Jiahui Hu +4
Federated Learning (FL) exhibits privacy vulnerabilities under gradient inversion attacks (GIAs), which can extract private information from individual gradients. To enhance privac…
cs.CR2024★ 1 cited
Textual Unlearning Gives a False Sense of Unlearning
Jiacheng Du, Zhibo Wang, Jie Zhang +3
Language Models (LMs) are prone to ''memorizing'' training data, including substantial sensitive user information. To mitigate privacy risks and safeguard the right to be forgotten…
cs.CR2024★ 1 cited
SoK: On Gradient Leakage in Federated Learning
Jiacheng Du, Jiahui Hu, Zhibo Wang +4
Federated learning (FL) facilitates collaborative model training among multiple clients without raw data exposure. However, recent studies have shown that clients' private training…