4 citations · 15 across the 11 of their papers we have counts for
6 papers · 1 filter
Label Leakage Attacks in Machine Unlearning: A Parameter and Inversion-Based Approach
Weidong Zheng, Kongyang Chen, Yao Huang +2
With the widespread application of artificial intelligence technologies in face recognition and other fields, data privacy security issues have received extensive attention, especi…
Private Data Leakage in Federated Human Activity Recognition for Wearable Healthcare Devices
Kongyang Chen, Dongping Zhang, Sijia Guan +3
Wearable data serves various health monitoring purposes, such as determining activity states based on user behavior and providing tailored exercise recommendations. However, the in…
Machine Unlearning in Large Language Models
Kongyang Chen, Zixin Wang, Bing Mi +4
Recently, large language models (LLMs) have emerged as a notable field, attracting significant attention for its ability to automatically generate intelligent contents for various…
EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems
Kongyang Chen, Yi Lin, Hui Luo +4
In contemporary edge computing systems, decentralized edge nodes aggregate unprocessed data and facilitate data analytics to uphold low transmission latency and real-time data proc…
Membership Information Leakage in Federated Contrastive Learning
Kongyang Chen, Wenfeng Wang, Zixin Wang +3
Federated Contrastive Learning (FCL) represents a burgeoning approach for learning from decentralized unlabeled data while upholding data privacy. In FCL, participant clients colla…
BAGEL: Backdoor Attacks against Federated Contrastive Learning
Yao Huang, Kongyang Chen, Jiannong Cao +5
Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a glob…