arXiv cs.AI· Jiajie He, Xintong Chen, Xinyang Fang, Min-Chun Chen, Yuechun Gu, Wenjin Liu, Keke Chen·· 14 小时前AI 评分22
推荐系统成员推理攻击（MIA）综述
Membership Inference Attacks on Recommender System: A Survey
AI 导读
一篇综述首次系统梳理了推荐系统（RecSys）上的成员推理攻击（MIA），这类攻击可推断某条用户交互记录是否被用于训练目标模型，从而直接造成隐私泄露。文章指出传统 MIA 因推荐系统存在不可见的后验概率而不适用，并给出按特征划分的统一分类体系，分析各类方法的优缺点及攻防挑战。该综述已被 ACM TOIS 送审，并基于现有局限提出若干未来研究方向。
正文 · 原文
Abstract:Recommender systems (RecSys) have been widely applied to various applications, including E-commerce, finance, healthcare, social media and have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. However, recent studies have shown that RecSys are vulnerable to membership inference attacks (MIAs), which aim to infer whether user interaction record was used to train a target model or not. MIAs on RecSys models can directly lead to a privacy breach. For example, via identifying the fact that a purchase record that has been used to train a RecSys associated with a specific user, an attacker can infer that user's special quirks. In recent years, MIAs have been shown to be effective on other ML tasks, e.g., classification models and natural language processing. However, traditional MIAs are ill-suited for RecSys due to the unseen posterior probability. Although MIAs on RecSys form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this article, we conduct the first comprehensive survey on RecSys MIAs. This survey offers a comprehensive review of the latest advancements in RecSys MIAs, exploring the design principles, challenges, attack and defense associated with this emerging field. We provide a unified taxonomy that categorizes different RecSys MIAs based on their characterizations and discuss their pros and cons. Based on the limitations and gaps identified in this survey, we point out several promising future research directions to inspire the researchers who wish to follow this area. This survey not only serves as a reference for the research community but also provides a clear description for researchers outside this research domain.
| Comments: | under review in ACM TOIS |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2509.11080 [cs.IR] |
| (or arXiv:2509.11080v5 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2509.11080 arXiv-issued DOI via DataCite |
Submission history
From: Jiajie He [view email]
[v1]
Sun, 14 Sep 2025 04:06:03 UTC (37 KB)
[v2]
Mon, 27 Oct 2025 05:12:35 UTC (45 KB)
[v3]
Thu, 8 Jan 2026 00:15:19 UTC (1,660 KB)
[v4]
Fri, 11 Sep 2026 02:20:08 UTC (1,111 KB)
[v5]
Thu, 29 Oct 2026 02:29:26 UTC (3,404 KB)
来源：arXiv cs.AI · arxiv.org