Poisoning Decentralized Collaborative Recommender System and Its Countermeasures
arxiv(2024)
摘要
To make room for privacy and efficiency, the deployment of many recommender
systems is experiencing a shift from central servers to personal devices, where
the federated recommender systems (FedRecs) and decentralized collaborative
recommender systems (DecRecs) are arguably the two most representative
paradigms. While both leverage knowledge (e.g., gradients) sharing to
facilitate learning local models, FedRecs rely on a central server to
coordinate the optimization process, yet in DecRecs, the knowledge sharing
directly happens between clients. Knowledge sharing also opens a backdoor for
model poisoning attacks, where adversaries disguise themselves as benign
clients and disseminate polluted knowledge to achieve malicious goals like
promoting an item's exposure rate. Although research on such poisoning attacks
provides valuable insights into finding security loopholes and corresponding
countermeasures, existing attacks mostly focus on FedRecs, and are either
inapplicable or ineffective for DecRecs. Compared with FedRecs where the
tampered information can be universally distributed to all clients once
uploaded to the cloud, each adversary in DecRecs can only communicate with
neighbor clients of a small size, confining its impact to a limited range. To
fill the gap, we present a novel attack method named Poisoning with Adaptive
Malicious Neighbors (PAMN). With item promotion in top-K recommendation as the
attack objective, PAMN effectively boosts target items' ranks with several
adversaries that emulate benign clients and transfers adaptively crafted
gradients conditioned on each adversary's neighbors. Moreover, with the
vulnerabilities of DecRecs uncovered, a dedicated defensive mechanism based on
user-level gradient clipping with sparsified updating is proposed. Extensive
experiments demonstrate the effectiveness of the poisoning attack and the
robustness of our defensive mechanism.
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