Reference : Privacy-Preserving Context-Aware Recommender Systems: Analysis and New Solutions
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/24361
Privacy-Preserving Context-Aware Recommender Systems: Analysis and New Solutions
English
Tang, Qiang mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Wang, Jun [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Sep-2015
Computer Security - ESORICS 2015 - 20th European Symposium on Research in Computer Security
Yes
International
Computer Security - ESORICS 2015 - 20th European Symposium on Research in Computer Security
September 21-25, 2015
[en] Nowadays, recommender systems have become an indispens-
able 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 incorpo-
rating 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 pro-
tocol outputs. Finally, we use the well-known MovieLens 100k dataset to
evaluate the performances for different parameter sizes.
SnT
Fonds National de la Recherche - FnR
Researchers
http://hdl.handle.net/10993/24361
FnR ; FNR5856658 > Qiang Tang > BRAIDS > Boosting Security and Efficiency in Recommender Systems > 15/04/2014 > 14/04/2017 > 2013

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