An Enhanced Middleware for Collaborative Privacy in IPTV Recommender Services
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One of the concerns users have to confronted when using IPTV system is the information overload that makes it difficult for them to find a suitable content according to their personal preferences. Recommendation service is one of the most widely adopted technologies for alleviating this problem, these services intend to provide people with referrals of items they will appreciate based on their preferences. IPTV users must ensure their sensitive preferences collected by any recommendation service are properly secured. In this work, we introduce a framework for private recommender service based on Enhanced Middleware for Collaborative Privacy (EMCP). EMCP executes a two-stage concealment process that gives the user a complete control on the privacy level of his/her profile. We utilize trust mechanism to augment the accuracy and privacy of the recommendations. Trust heuristic spot users who are trustworthy with respect to the user requesting the recommendation. Later, the neighborhood formation is calculated using proximity metrics based on these trustworthy users. Finally, Users submit their profiles in an obfuscated form without revealing any information about their data, and the computation of recommendations proceeds over the obfuscated data using secure multiparty computation protocol. We expand the obfuscation scope from single obfuscation level for all users to arbitrary obfuscation levels based on trustworthy between users. In other words, we correlate the obfuscation level with different trust levels, so the more trusted a target user is the less obfuscation copy of profile he can access. We also provide an IPTV network scenario and experimentation results. Our results and analysis show that our two-stage concealment process not only protects the privacy of users but also can maintain the recommendations accuracy.
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