Privacy Preserving-Based Recommendation Service Model of Mobile Commerce and Anonimity Algorithm

Abstract

The wide location based service in the application of mobile commerce has brought great convenience to people's work and lives, while the risk of privacy disclosure has been receiving more and more attention from the academia and industry. After analyzing the privacy issues in mobile commerce, a privacy preserving recommendation service framework based on cloud is established. According to the defined personalized privacy requirements of mobile users, the (K, L, P)-anonymity model is formally described. Based on the anonymity model, a dynamically structure minimum anonymous sets algorithm DSMAS is proposed, which can be used to protect the location, identifier and other sensitive information of the mobile user on the road network. Finally, based on a real road network and generated privacy profiles of the mobile users, the feasibility of algorithm are validated by experimentally analyzing using the metrics including information entropy, query cost, anonymization time and dummy ratio.