Personalized Privacy-Preserving Semi-Centralized Recommendation System in a Trust-Based Agent Network

In the current big data era, recommendation systems play an important role in our daily life to help us make faster and better decisions from massive numbers of choices. Personalized recommendation has gained its popularity as it provides recommendations according to the user profile, preferences and/or interests. Many existing systems make recommendation by centralizing data. However, the exposure of sensitive data raises a privacy concern as research has shown that it is possible to de-identify anonymous users. Examples include inferring sensitive information (e.g., political views, sexual orientations) from nonsensitive data (e.g., movie ratings). In this paper, we present a personalized privacy-preserving recommendation system called Trust-based Agent Network (TAN). It tackles the privacy issue by semi-decentralizing data and treating each node in the network as an agent. As such, data are distributed to each agent within each trusted network, and the recommendation service provider collects only obfuscated data from agents by adopting the differential-privacy mechanism. Consequently, data in our TAN are either protected inside local trusted networks or obfuscated outside of trusted networks. Final recommendation can then be made by aggregating the local suggestions from the trusted network and obfuscated global suggestions from the service provider. Personalized recommendations can be made by putting more emphasize on local suggestions. Evaluation results show that our TAN leads to high accuracy and highly personalized recommendations while protecting privacy.