31 citations · 42 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
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…
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning
Hongsheng Hu, Xuyun Zhang, Zoran Salcic +3
Federated learning (FL) is a popular approach to facilitate privacy-aware machine learning since it allows multiple clients to collaboratively train a global model without granting…
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…
Membership Inference via Backdooring
Hongsheng Hu, Zoran Salcic, Gillian Dobbie +3
Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant individuals the right to be forgotten. In the context of machine learning, this requir…
Source Inference Attacks in Federated Learning
Hongsheng Hu, Zoran Salcic, Lichao Sun +2
Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows multiple clients to jointly train a model without sharing their private data. Recently, many s…