Dynamical Credibility Assessment of Privacy-Preserving Strategy for Opportunistic Mobile Crowd Sensing

Mobile crowd sensing (MCS) is becoming a popular paradigm to collect information, which has the potential to change people's life. However, MCS is vulnerable to security threats due to the increasing reliance on communication and computing. The challenges of unique security and privacy caused by MCS include privacy protection, integrity, confidentiality, and availability. To tackle these issues concurrently, we present the design of a dynamical credibility assessment of privacy-preserving (CAPP) strategy, a novel credibility assessment-based solution to protect privacy in opportunistic MCS, which is able to cope with malicious attacks and privacy protection even against intelligent MCS entities. In CAPP, the sensing data are dynamically split into slices and the number of slices is based on the trust of encountered nodes. Specially, node trust is assessed in two dimensions including the quality of contribution trust and social trust, which indicates how likely a node can fulfill its functionality and how trustworthy the relationship between a node and other nodes will be, respectively. Moreover, the secret sharing scheme and an anonymous strategy ensure the data integrity and the anonymity of participants. The effectiveness in privacy protection and efficiency of the proposed scheme are validated through theoretical analysis and numerical results.