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

Title"You Might Also Like:" Privacy Risks of Collaborative Filtering
Publication TypeConference Paper
Year of Publication2011
AuthorsCalandrino, JA, Kilzer, A, Narayanan, A, Felten, EW, Shmatikov, V
Conference NameSecurity and Privacy (SP), 2011 IEEE Symposium on
Date PublishedMay
Keywordsaccuracy, Amazon, collaboration, collaborative filtering, commercial Web sites, consumer behaviour, Covariance matrix, customer transactions, data privacy, groupware, History, Hunch, Inference algorithms, inference attacks, inference mechanisms, information filtering, Internet, Internet user, Last.fm, Library Thing, privacy, privacy risks, recommender systems, Web sites
Abstract

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.

DOI10.1109/SP.2011.40
AttachmentSize
PDF icon Youmightlike2011Calandrino.pdf431.62 KB