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2015 | OriginalPaper | Buchkapitel

Privacy-Preserving Context-Aware Recommender Systems: Analysis and New Solutions

verfasst von : Qiang Tang, Jun Wang

Erschienen in: Computer Security -- ESORICS 2015

Verlag: Springer International Publishing

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Abstract

Nowadays, recommender systems have become an indispensable part of our daily life and provide personalized services for almost everything. However, nothing is for free – such systems have also upset the society with severe privacy concerns because they accumulate a lot of personal information in order to provide recommendations. In this work, we construct privacy-preserving recommendation protocols by incorporating cryptographic techniques and the inherent data characteristics in recommender systems. We first revisit the protocols by Jeckmans et al. and show a number of security issues. Then, we propose two privacy-preserving protocols, which compute predicted ratings for a user based on inputs from both the user’s friends and a set of randomly chosen strangers. A user has the flexibility to retrieve either a predicted rating for an unrated item or the Top-N unrated items. The proposed protocols prevent information leakage from both protocol executions and the protocol outputs. Finally, we use the well-known MovieLens 100k dataset to evaluate the performances for different parameter sizes.

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Vorheriges Kapitel Privacy-Preserving Observation in Public Spaces

Nächstes Kapitel Rich Queries on Encrypted Data: Beyond Exact Matches

Nur mit Berechtigung zugänglich

http://grouplens.org/datasets/movielens/.

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Titel: Privacy-Preserving Context-Aware Recommender Systems: Analysis and New Solutions
verfasst von: Qiang Tang
Jun Wang
Verlag: Springer International Publishing
Buch: Computer Security -- ESORICS 2015
Print ISBN: 978-3-319-24176-0

Electronic ISBN: 978-3-319-24177-7

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-24177-7_6

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