P3: A Privacy Preserving Personalization Middleware for recommendation-based services

We propose the design of a privacy-preservingpersonalization middleware that enables the enduser to avail of personalized services without disclosing sensitive profile information to the content/service-provider or any third party for that matter. Our solution relies on a distributed infrastructure comprising local clients running on end-user devices and a set of middleware nodes that could be collaboratively donated by few end-users or hosted by multiple non-colluding third parties. The key idea is to locally compute the user’s profile on the device, locally determine the interest group of the user wherein an interest-group will comprise users with similar interest, and anonymously aggregate the collective behaviour of the members of the interest group at some middleware node to generate recommendations for the group members. In addition, our system is also open for third party content and recommendation injection without leaking the users privacy.