Holistic Collaborative Privacy Framework for Users' Privacy in Social Recommender Service

The current business model for existing recommender services is centered around the availability of users' personal data at their side whereas consumers have to trust that the recommender service providers will not use their data in a malicious way. With the increasing number of cases for privacy breaches, different countries and corporations have issued privacy laws and regulations to define the best practices for the protection of personal information. The data protection directive 95/46/EC and the privacy principles established by the Organization for Economic Cooperation and Development (OECD) are examples of such regulation frameworks. In this paper, we assert that utilizing third-party recommender services to generate accurate referrals are feasible, while preserving the privacy of the users' sensitive information which will be residing on a clear form only on his/her own device. As a result, each user who benefits from the third-party recommender service will have absolute control over what to release from his/her own preferences. We proposed a collaborative privacy middleware that executes a two stage concealment process within a distributed data collection protocol in order to attain this claim. Additionally, the proposed solution complies with one of the common privacy regulation frameworks for fair information practice in a natural and functional way -which is OECD privacy principles. The approach presented in this paper is easily integrated into the current business model as it is implemented using a middleware that runs at the end-users side and utilizes the social nature of content distribution services to implement a topological data collection protocol

Privacy Aware Recommender Service using Multi-agent Middleware – An IPTV Network Scenario

IPTV service providers are starting to realize the significant value of recommender services in attracting and satisfying customers as they offer added values e.g. by delivering suitable personalized contents according to customers personal interests in a seamless way, increase content sales and gain competitive advantage over other competitors. However the current implementations of recommender services are mostly centralized combined with collecting data from multiple users that cover personal preferences about different contents they watched or purchased. These profiles are stored at third-party providers that might be operating under different legal jurisdictions related to data privacy laws rather than the ones applied where the service is consumed. From privacy perspective, so far they are all based on either a trusted third party model or on some generalization model. In this work, we address the issue of maintaining users ’ privacy when using third-party recommender services and introduce a framework for Private Recommender Service (PRS) based on Enhanced Middleware for Collaborative Privacy (EMCP) running at user side. In our framework, PRS uses platform for privacy preferences (P3P) policies for specifying their data usage practices. While EMCP allows the users to use P3P policies exchange language (APPEL) for specifying their privacy preferences for the data extracted from their profiles. Moreover, EMCP executes a two-stage concealment process on the extracted dat