Privacy-preserving cost-effective work distribution with fine-grained access control for mobile crowdsensing

Abstract

Crowdsensing is a new paradigm for data collection in the internet of things environment, in which the data aspirant outsource sensing tasks to sensing devices such as smart mobile phones through a crowdsensing server (CSS). Despite its numerous advantages, the vulnerability of sensitive data disclosure in crowdsensing generates a severe obstacle against its participants and restricts its use in privacy-sensitive domains. The semi-honest CSS tries to learn sensitive information such as the identity, and attributes of both the data requester and data collectors. Also, the malicious data collector nodes who are unable to execute the data collection task will make an effort to learn the task description and thereby infer the data being collected. We propose a privacy-preserving cost-effective work distribution system with a fine-grained access control (PPA) scheme. A ciphertext-policy attribute-based encryption (CP-ABE) method with hidden access policy is used to choose data collectors and safeguard both the data requester and data collector's privacy.