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题名

Poisoning Decentralized Collaborative Recommender System and Its Countermeasures

作者
通讯作者Shi, Yuhui
DOI
发表日期
2024-07-10
会议名称
47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024
ISBN
9798400704314
会议录名称
页码
1712-1721
会议日期
July 14, 2024 - July 18, 2024
会议地点
Washington, DC, United states
会议录编者/会议主办者
ACM SIGIR
出版地
1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
出版者
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. 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.
© 2024 ACM.
关键词
学校署名
第一 ; 通讯
语种
英语
相关链接[来源记录]
收录类别
资助项目
This work is partially supported by the National Key R&D Program of China under the Grant No. 2023YFE0106300 and 2017YFC0804002, Australian Research Council under the streams of Future Fellowship (Grant No. FT210100624), Discovery Early Career Researcher Award (Grant No. DE230101033), Discovery Project (Grants No. DP240101108, and No. DP240101814), Shenzhen Fundamental Research Program under the Grant No. JCYJ20200109141235597, and National Science Foundation of China under Grant No. 62250710682 and 61761136008.
WOS研究方向
Computer Science
WOS类目
Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号
WOS:001273410001077
EI入藏号
20243216840089
EI主题词
Knowledge management ; Learning systems
EI分类号
Computer Applications:723.5 ; Information Retrieval and Use:903.3
来源库
EV Compendex
引用统计
被引频次[WOS]:1
成果类型会议论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/807088
专题南方科技大学
作者单位
1.Southern University of Science and Technology, Shenzhen, China
2.The University of Queensland, Brisbane, Australia
3.University of Electronic Science and Technology of China, Chengdu, China
4.The University of Queensland, Brisbane; QLD, Australia
第一作者单位南方科技大学
通讯作者单位南方科技大学
第一作者的第一单位南方科技大学
推荐引用方式
GB/T 7714
Zheng, Ruiqi,Qu, Liang,Chen, Tong,et al. Poisoning Decentralized Collaborative Recommender System and Its Countermeasures[C]//ACM SIGIR. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:Association for Computing Machinery, Inc,2024:1712-1721.
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