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Privacy-Preserving Context-Aware Recommender Systems: Analysis and New Solutions
Tang, Qiang; Wang, Jun
2015In Computer Security - ESORICS 2015 - 20th European Symposium on Research in Computer Security
Peer reviewed
 

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Abstract :
[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.
Research center :
SnT
Disciplines :
Computer science
Author, co-author :
Tang, Qiang ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Wang, Jun ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Privacy-Preserving Context-Aware Recommender Systems: Analysis and New Solutions
Publication date :
September 2015
Event name :
Computer Security - ESORICS 2015 - 20th European Symposium on Research in Computer Security
Event date :
September 21-25, 2015
Audience :
International
Main work title :
Computer Security - ESORICS 2015 - 20th European Symposium on Research in Computer Security
Peer reviewed :
Peer reviewed
FnR Project :
FNR5856658 - Boosting Security And Efficiency In Recommender Systems, 2013 (15/04/2014-14/04/2017) - Qiang Tang
Name of the research project :
BRAIDS
Funders :
FNR - Fonds National de la Recherche [LU]
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since 08 February 2016

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