Enabling Data Trustworthiness and User Privacy in Mobile Crowdsensing

Abstract

Ubiquitous mobile devices with rich sensors and advanced communication capabilities have given rise to mobile crowdsensing systems. The diverse reliabilities of mobile users and the openness of sensing paradigms raise concerns for data trustworthiness, user privacy, and incentive provision. Instead of considering these issues as isolated modules in most existing researches, we comprehensively capture both conflict and inner-relationship among them. In this paper, we propose a holistic solution for trustworthy and privacy-aware mobile crowdsensing with no need of a trusted third party. Specifically, leveraging cryptographic technologies, we devise a series of protocols to enable benign users to request tasks, contribute their data, and earn rewards anonymously without any data linkability. Meanwhile, an anonymous trust/reputation model is seamlessly integrated into our scheme, which acts as reference for our fair incentive design, and provides evidence to detect malicious users who degrade the data trustworthiness. Particularly, we first propose the idea of limiting the number of issued pseudonyms which serves to efficiently tackle the anonymity abuse issue. Security analysis demonstrates that our proposed scheme achieves stronger security with resilience against possible collusion attacks. Extensive simulations are presented which demonstrate the efficiency and practicality of our scheme.