ATM: Attribute-Based Privacy-Preserving Task Assignment and Incentive Mechanism for Crowdsensing

Abstract

Crowdsensing is a practical component in Internet-of-Things, in which task requesters outsource sensing tasks to workers by a crowdsensing server (CS). Task assignment and incentive design are two essential parts of crowdsensing. However, due to the semi-honest CS and malicious workers, there exist several security and fairness issues. First, during task assigning and reward distributing, CS can get the private information of both task requester and workers such as identity, attributes. Second, malicious workers who are unable to execute tasks can get the task content, which leaks task privacy. Third, most auction-based incentive mechanism only considers the biddings of workers and ignores the real capabilities of workers. To this end, we propose a privacy-preserving fine-grained task assignment scheme for the crowdsensing system. Moreover, we also design a novel incentive mechanism based on the capabilities of workers. To be specific, a ciphertext-policy attribute-based encryption (CP-ABE) scheme with the characteristics of hidden policy is adopted to select workers and protect the privacy that of both requirements and workers’ attributes. We quantify the capabilities of workers based on the workers’ attributes and further determine the workers’ reward according to their capabilities. Detailed security analysis demonstrates that our proposed scheme can protect workers’ identity privacy, attribute privacy, and data confidentiality. Besides, the extensive experiments show that our incentive mechanism can effectively motivate workers.

Keywords

• Crowdsensing
• attribute-based encryption
• privacy preservation
• task assignment
• incentive mechanism