AGAS

An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems This is the official code to the paper: "An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems". This paper introduces AGAS, which is an LLM-driven shilling attack against black-box...

Phantom Subgroup Poisoning: Stealth Attacks on Federated Recommender Systems

Federated recommender systems FedRec have emerged as a promising solution for delivering personalized recommendations while safeguarding user privacy. However, recent studies have demonstrated their vulnerability to poisoning attacks. Existing attacks typically target the entire user group, which...

RAID: an In-Training Defense against Attribute Inference Attacks in Recommender Systems

In various networks and mobile applications, users are highly susceptible to attribute inference attacks, with particularly prevalent occurrences in recommender systems. Attackers exploit partially exposed user profiles in recommendation models, such as user embeddings, to infer private attribute...