May 14, 2024, 4:11 a.m. | Xiaoxiao Chi, Xuyun Zhang, Yan Wang, Lianyong Qi, Amin Beheshti, Xiaolong Xu, Kim-Kwang Raymond Choo, Shuo Wang, Hongsheng Hu

cs.CR updates on arXiv.org arxiv.org

arXiv:2405.07018v1 Announce Type: new
Abstract: Recommender systems have been successfully applied in many applications. Nonetheless, recent studies demonstrate that recommender systems are vulnerable to membership inference attacks (MIAs), leading to the leakage of users' membership privacy. However, existing MIAs relying on shadow training suffer a large performance drop when the attacker lacks knowledge of the training data distribution and the model architecture of the target recommender system. To better understand the privacy risks of recommender systems, we propose shadow-free MIAs …

applications arxiv attacks cs.cr free large performance privacy recommender systems shadow studies systems thought training vulnerable

Sr. Product Manager

@ MixMode | Remote, US

Information Security Engineers

@ D. E. Shaw Research | New York City

Technology Security Analyst

@ Halton Region | Oakville, Ontario, Canada

Senior Cyber Security Analyst

@ Valley Water | San Jose, CA

Engineer I, S/W QA Cyber Security

@ Boston Scientific | Pune, IN

Application Security and Secure-SDLC Expert

@ CYE | Herzliya, Israel