Privacy and security in crowdsensing

Abstract

The rapid proliferation of sensor-embedded devices has enabled crowdsensing, a new paradigm which effectively collects sensing data from pervasive users. However, both the openness of crowdsensing systems and the richness of users' submitted data raise significant concerns for privacy and security. In this thesis, we aim to identify and solve privacy and security issues in crowdsensing. Specifically, we consider three important parts in crowdsensing: task allocation, incentive mechanisms, and truth discovery. In crowdsensing systems, task allocation is used to select a proper subset of users to perform tasks. Incentive mechanisms are used to stimulate users to participate in the system. Truth discovery is used to aggregate data. We first analyze privacy issues in task allocation and incentive mechanisms raised by the inference attack in which a user is able to infer other users' sensitive information according to published information. We propose two task allocation algorithms which defend against location-inference attack. To protect users' bid privacy from inference attack, we propose two frameworks for privacy-preserving incentive mechanisms. Then, we analyze the security issues in incentive mechanisms and truth discovery raised by the Sybil attack in which a user illegitimately pretends to be multiple identities to gain benets. To deter users from conducting a Sybil attack, we propose Sybil-proof incentive mechanisms for both offline and online scenarios. Additionally, we propose a Sybil-resistant truth discovery framework to diminish the impact of the Sybil attack on the aggregated data. Both simulation and experiment results show the effectiveness of the proposed works in solving privacy and security issues in crowdsensing.