Work

Membership Inference Attacks Against Recommender Systems

Year: 2021

Type: conference-paper

Abstract: Recently, recommender systems have achieved promising performances and become one of the most widely used web applications. However, recommender systems are often trained on highly sensitive user data, thus potential data leakage from recommender systems may lead to severe privacy problems.

Source: ACM SIGSAC Conference on Computer and Communications Security (CCS)

DOI: 10.1145/3460120.3484770

Authors: Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhunmin Chen, Pengfei Hu, Yang Zhang

Institutions: Shandong University, Helmholtz Center for Information Security

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Shandong University, Tencent, National Natural Science Foundation of China

Awards: 基于多源异构数据融合的可解释性医疗健康问答技术研究, G4278653462, G4890194082, G69085316, 面向主要依据隐反馈数据的会话型推荐系统及功能增强研究

OpenAlex ID: W3211930400

FWCI: 4.607

Cites: 61

Cited by: 85

Related to: 10