Experimental Demonstration of a Hybrid Privacy-Preserving Recommender System

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.

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