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2012 Seventh International Conference on Availability, Reliability and Security (2008)
Mar. 4, 2008 to Mar. 7, 2008
ISBN: 978-0-7695-3102-1
pp: 161-170
Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. We report on the first experimental realization of a theoretical framework called ALAMBIC, which we had previously put forth to protect the privacy of customers and the commercial interests of merchants. Our system is a hybrid recommender that combines content-based, demographic and collaborative filtering techniques.??The originality of our approach is to split customer data between the merchant and a semi-trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system can only be subverted by a coalition between these two parties. Experimental results confirm that the performance and user-friendliness of the application need not suffer from the adoption of such privacy-protection solutions. Furthermore, user testing of our prototype show that users react positively to the privacy model proposed.
privacy protection, recommender systems, hybrid recommender systems
Esma Aimeur, Jose M. Fernandez, Gilles Brassard, Zbigniew Rakowski, Flavien Serge Mani Onana, "Experimental Demonstration of a Hybrid Privacy-Preserving Recommender System", 2012 Seventh International Conference on Availability, Reliability and Security, vol. 00, no. , pp. 161-170, 2008, doi:10.1109/ARES.2008.193
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