A Variable Weight Privacy-Preserving Algorithm for the Mobile Crowd Sensing Network

Abstract

Mobile crowd sensing (MCS) network collects scenario, environmental, and individual data within a specific range via the intelligent sensing equipment carried by the mobile users, thus providing social decision-making services. MCS is emerging as a most important sensing paradigm. However, the person-centered sensing itself carries the risk of divulging users’ privacy. To address this problem, we proposed a variable weight privacy-preserving algorithm of secure multiparty computation. This algorithm is based on privacy-preserving utility and its effectiveness and feasibility are demonstrated through experiment.