privacy-preserving demographic filtering
The use of recommender systems in e-commerce to guide customer choices presents a privacy protection problem that is twofold. We seek to protect the privacy interests of customers by trying to keep private their identity and demographic characteristics, and possibly also their buying preferences and behaviour. This can be desirable even if anonymity is used. Furthermore, we want to protect the commercial interests of the e-commerce service providers by allowing them to make recommendations as accurate as possible, without unnecessarily revealing valuable information they have legitimately accumulated, such as market trends, to third parties.In this paper, we concentrate on recommender systems based on demographic filtering , which make recommendations based on feedback of previous users of similar demographic characteristics (such as age, sex, level of education, wealth, geographical location, etc.). We propose a system called ALAMBIC, which adequately achieves the above privacy-protection objectives in this kind of recommender systems. Our system is based on a semi-trusted third party in which the users need only have limited confidence. A main originality of our approach is to split user data between that party and the service provider in such a way that neither can derive sensitive information from their share alone.
demographic filtering recommender system e-commerce privacy protection semi-trusted third party architecture clustering