"You Might Also Like:" Privacy Risks of Collaborative Filtering

被引：192

作者：

Calandrino, Joseph A. ^{[1
]}

Kilzer, Ann ^{[2
]}

Narayanan, Arvind ^{[3
]}

Felten, Edward W. ^{[1
]}

Shmatikov, Vitaly ^{[2
]}

机构：

[1] Princeton Univ, Dept Comp Sci, Princeton, NJ 08544 USA

[2] Univ Texas Austin, Dept Comp Sci, Austin, TX USA

[3] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA

来源：

2011 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP 2011) | 2011年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/SP.2011.40

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Many commercial websites use recommender systems to help customers locate products and content. Modern recommenders are based on collaborative filtering: they use patterns learned from users' behavior to make recommendations, usually in the form of related-items lists. The scale and complexity of these systems, along with the fact that their outputs reveal only relationships between items (as opposed to information about users), may suggest that they pose no meaningful privacy risk. In this paper, we develop algorithms which take a moderate amount of auxiliary information about a customer and infer this customer's transactions from temporal changes in the public outputs of a recommender system. Our inference attacks are passive and can be carried out by any Internet user. We evaluate their feasibility using public data from popular websites Hunch, Last.fm, LibraryThing, and Amazon.

引用

页码：231 / 246

页数：16