PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion-Reference-Cited by-同舟云学术

PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion

Published:2022-02-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
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Author:

Zhang Shijie¹,Yin Hongzhi¹,Chen Tong¹,Huang Zi¹,Nguyen Quoc Viet Hung²,Cui Lizhen³

Affiliation:

1. The University of Queensland, Brisbane, QLD, Australia

2. Griffith University, Gold Coast, Australia

3. ShanDong University, Jinan, China

Funder

Australian Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3488560.3498386

Reference42 articles.

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2. Himan Abdollahpouri , Masoud Mansoury , Robin Burke , and Bamshad Mobasher . 2019 . The unfairness of popularity bias in recommendation . In RMSE Workshop . Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. In RMSE Workshop .

3. Eugene Bagdasaryan Andreas Veit Yiqing Hua Deborah Estrin and Vitaly Shmatikov. 2020. How to backdoor federated learning. In AISTATS. 2938--2948. Eugene Bagdasaryan Andreas Veit Yiqing Hua Deborah Estrin and Vitaly Shmatikov. 2020. How to backdoor federated learning. In AISTATS. 2938--2948.

4. Gilad Baruch Moran Baruch and Yoav Goldberg. 2019. A Little Is Enough: Circumventing Defenses For Distributed Learning. In NeurIPS . 8632--8642. Gilad Baruch Moran Baruch and Yoav Goldberg. 2019. A Little Is Enough: Circumventing Defenses For Distributed Learning. In NeurIPS . 8632--8642.

5. Arjun Nitin Bhagoji Supriyo Chakraborty Prateek Mittal and Seraphin Calo. 2019. Analyzing federated learning through an adversarial lens. In ICML. 634--643. Arjun Nitin Bhagoji Supriyo Chakraborty Prateek Mittal and Seraphin Calo. 2019. Analyzing federated learning through an adversarial lens. In ICML. 634--643.

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