Decentralized Collaborative Recommender Systems: Vulnerabilities to Poisoning Attacks and Countermeasures

Decentralized collaborative recommender systems (DecRecs) are vulnerable to model poisoning attacks, where adversaries disguise themselves as benign clients and disseminate polluted knowledge to promote target items. This paper proposes a novel attack method, Poisoning with Adaptive Malicious Neighbors (PAMN), that effectively boosts target items' ranks by adaptively crafting gradients based on each adversary's neighbors. To counter these threats, a dedicated defensive mechanism, User-level Clipping with Sparsified Updating (UCSU), is introduced to neutralize the impact of poisoning attacks at the user level.