Membership Inference Attacks Against Recommender Systems

Author:

Zhang Minxing1,Ren Zhaochun2,Wang Zihan2,Ren Pengjie2,Chen Zhunmin2,Hu Pengfei2,Zhang Yang3

Affiliation:

1. Shandong University & CISPA Helmholtz Center for Information Security, Qingdao, China

2. Shandong University, Qingdao, China

3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

Funder

Hybrid Intelligence Center

Natural Science Foundation of China

A 10-year program funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research, https://hybrid-intelligence-centre.nl

Shandong University multidisciplinary research and innovation team of young scholars

National Key R&D Program of China

Tencent WeChat Rhino-Bird Focused Research Program

the Helmholtz Association within the project ``Trustworthy Federated Data Analytics' (TFDA)

Key Scientific and Technological Innovation Program of Shandong Province

Publisher

ACM

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1. HAMIATCM: high-availability membership inference attack against text classification models under little knowledge;Applied Intelligence;2024-06-19

2. Please Tell Me More: Privacy Impact of Explainability through the Lens of Membership Inference Attack;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

3. Test-Time Poisoning Attacks Against Test-Time Adaptation Models;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

4. Machine learning security and privacy: a review of threats and countermeasures;EURASIP Journal on Information Security;2024-04-23

5. Defense Against Model Extraction Attacks on Recommender Systems;Proceedings of the 17th ACM International Conference on Web Search and Data Mining;2024-03-04

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