PORE: Provably Robust Recommender Systems against Data Poisoning Attacks

Autor:	Jia, Jinyuan, Liu, Yupei, Hu, Yuepeng, Gong, Neil Zhenqiang
Rok vydání:	2023
Předmět:	Computer Science - Cryptography and Security Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Data poisoning attacks spoof a recommender system to make arbitrary, attacker-desired recommendations via injecting fake users with carefully crafted rating scores into the recommender system. We envision a cat-and-mouse game for such data poisoning attacks and their defenses, i.e., new defenses are designed to defend against existing attacks and new attacks are designed to break them. To prevent such a cat-and-mouse game, we propose PORE, the first framework to build provably robust recommender systems in this work. PORE can transform any existing recommender system to be provably robust against any untargeted data poisoning attacks, which aim to reduce the overall performance of a recommender system. Suppose PORE recommends top-$N$ items to a user when there is no attack. We prove that PORE still recommends at least $r$ of the $N$ items to the user under any data poisoning attack, where $r$ is a function of the number of fake users in the attack. Moreover, we design an efficient algorithm to compute $r$ for each user. We empirically evaluate PORE on popular benchmark datasets. Comment: To appear in USENIX Security Symposium, 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.14601 Zobrazit plný text záznamu View this record from Arxiv