5 papers
Reference Recommendation based Membership Inference Attack against Hybrid-based Recommender Systems
Xiaoxiao Chi, Xuyun Zhang, Yan Wang +2
Recommender systems have been widely deployed across various domains such as e-commerce and social media, and intelligently suggest items like products and potential friends to use…
When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
Ruining Sun, Hongsheng Hu, Wei Luo +4
With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the rese…
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
Yongzhe Jia, Xuyun Zhang, Hongsheng Hu +5
Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model withou…
Watermarking Text Data on Large Language Models for Dataset Copyright
Yixin Liu, Hongsheng Hu, Xun Chen +2
Substantial research works have shown that deep models, e.g., pre-trained models, on the large corpus can learn universal language representations, which are beneficial for downstr…
Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought
Xiaoxiao Chi, Xuyun Zhang, Yan Wang +6
Recommender systems have been successfully applied in many applications. Nonetheless, recent studies demonstrate that recommender systems are vulnerable to membership inference att…