An Enhanced Middleware for Collaborative Privacy in IPTV Recommender Services

One of the concerns users have to confronted when using IPTV system is 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 to alleviating this problem; these services intend to provide people with referrals of items they will appreciate based upon 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 profile. We utilize trust mechanism to augment recommendation’s accuracy and privacy. Trust heuristic spot users whom are trustworthy with respect to the user requesting recommendation (target-user). Later, the neighbourhood 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 multi-party 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 users’ profile he can access. We also provide an IPTV network scenario and experimentation results. Our results and analysis shows that our two-stage concealment process not only protect the users’ privacy, but also can maintain the recommendations accuracy. KeywordsPrivacy; Clustering; IPTV Network; Recommendation-Services; Multi-agent