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Poisoning Decentralized Collaborative Recommender System and Its Countermeasures

Published: 11 July 2024 Publication History

Abstract

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. On the flip side, 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 (i.e., users) and transfers adaptively crafted gradients conditioned on each adversary's neighbors. A diversity-driven regularizer is further designed in PAMN to allow the adversaries to reach a broader group of multifaceted benign users. 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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  • (2024)Distributed Recommendation Systems: Survey and Research DirectionsACM Transactions on Information Systems10.1145/3694783Online publication date: 6-Sep-2024
  • (2024)Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided DiffusionACM Transactions on Information Systems10.1145/366608842:6(1-26)Online publication date: 19-Aug-2024
  • (2024)Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning AttacksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671795(3311-3322)Online publication date: 25-Aug-2024
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    cover image ACM Conferences
    SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2024
    3164 pages
    ISBN:9798400704314
    DOI:10.1145/3626772
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    Published: 11 July 2024

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    Author Tags

    1. decentralized collaborative learning
    2. on-device recommender system
    3. poisoning attack and defense

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    • Australian Research Council Discovery Early Career Researcher Award
    • Australian Research Council Discovery Project
    • Australian Research Council Future Fellowship

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    View all
    • (2024)Distributed Recommendation Systems: Survey and Research DirectionsACM Transactions on Information Systems10.1145/3694783Online publication date: 6-Sep-2024
    • (2024)Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided DiffusionACM Transactions on Information Systems10.1145/366608842:6(1-26)Online publication date: 19-Aug-2024
    • (2024)Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning AttacksProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671795(3311-3322)Online publication date: 25-Aug-2024
    • (2024)Brain Storm Optimization based Swarm Learning for Diabetic Retinopathy Image Classification2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612118(1-7)Online publication date: 30-Jun-2024

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