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Decentralized Collaborative Recommender Systems: Vulnerabilities to Poisoning Attacks and Countermeasures


Core Concepts
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.
Abstract
The paper discusses the vulnerabilities of decentralized collaborative recommender systems (DecRecs) to model poisoning attacks, where adversaries aim to promote target items by disseminating polluted knowledge. Key highlights: DecRecs are susceptible to poisoning attacks due to the client-to-client knowledge sharing, where adversaries can disguise themselves as benign users and transfer polluted gradients. The authors propose a novel attack method, PAMN, that adaptively crafts gradients based on each adversary's neighbors to effectively boost the exposure of target items. PAMN leverages a diversity-driven regularizer to encourage adversaries to communicate with a broader range of benign users, expanding their influence. To defend against these attacks, the authors introduce a user-level defense mechanism, UCSU, which employs gradient clipping and sparsified updating to neutralize the impact of poisoned gradients. Extensive experiments on real-world datasets demonstrate the effectiveness of the PAMN attack and the robustness of the UCSU defense.
Stats
"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." "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."
Quotes
"To fill the gap, we present a novel attack method named Poisoning with Adaptive Malicious Neighbors (PAMN). With item promotion in top-๐พrecommendation as the attack objective, PAMN effectively boosts target items' ranks with several adversaries that emulate benign clients (i.e., users) 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."

Deeper Inquiries

How can the proposed defense mechanism, UCSU, be extended to handle more sophisticated poisoning attack strategies in the future

To extend the UCSU defense mechanism to handle more sophisticated poisoning attack strategies in the future, several enhancements can be considered. One approach could involve incorporating anomaly detection techniques to identify abnormal behavior in the gradients shared by users. By detecting unusual patterns or outliers in the gradients, the defense mechanism can flag potential malicious activity and take proactive measures to mitigate the impact of sophisticated poisoning attacks. Additionally, implementing reinforcement learning algorithms to adaptively adjust the defense strategy based on the evolving attack patterns could enhance the resilience of UCSU against advanced poisoning strategies. Furthermore, integrating machine learning models for dynamic risk assessment and decision-making could enable UCSU to respond effectively to novel attack vectors and enhance its overall effectiveness in defending against sophisticated poisoning attacks.

What are the potential implications of the vulnerabilities uncovered in DecRecs on the broader landscape of decentralized machine learning systems

The vulnerabilities uncovered in DecRecs have significant implications for the broader landscape of decentralized machine learning systems. DecRecs' susceptibility to poisoning attacks highlights the critical need for robust security measures in decentralized collaborative systems to safeguard against malicious actors. The potential implications include a heightened awareness of the security risks associated with collaborative learning paradigms, emphasizing the importance of implementing effective defense mechanisms to protect sensitive data and ensure the integrity of the learning process. Furthermore, the vulnerabilities in DecRecs underscore the necessity for ongoing research and development in secure decentralized machine learning techniques to address emerging threats and enhance the resilience of decentralized systems against adversarial attacks. By addressing these vulnerabilities and implementing robust security measures, the broader landscape of decentralized machine learning systems can strengthen its defenses and mitigate the risks posed by malicious actors.

How can the diversity-driven regularization approach used in PAMN be applied to other types of decentralized collaborative systems to enhance their robustness against malicious actors

The diversity-driven regularization approach used in PAMN can be applied to other types of decentralized collaborative systems to enhance their robustness against malicious actors by promoting diversity among user profiles and interactions. By encouraging a diverse range of user preferences and behaviors, the regularization technique can help mitigate the impact of targeted attacks and enhance the overall resilience of decentralized collaborative systems. This approach can be particularly beneficial in scenarios where the system relies on collaborative learning and knowledge sharing among users, as it can help prevent adversaries from exploiting similarities in user behavior to launch effective poisoning attacks. By incorporating diversity-driven regularization in other decentralized collaborative systems, organizations can strengthen their security posture and reduce the risk of malicious manipulation of the learning process.
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