LPP-BPSI: A location privacy-preserving scheme using blockchain and Private Set Intersection in spatial crowdsourcing

Abstract

Leakage of location information can lead to malicious attacks on workers in spatial crowdsourcing (SC) and loss of security. In response, a location privacy preserving scheme based on blockchain and Private Set Intersection were proposed, which reduced the computational overhead of workers by using a cloud service provider for outsourcing secure computation while ensuring their location privacy. First, three smart contracts based on blockchain were designed that could eliminate the control of traditional spatial crowdsourcing servers on the task allocation process. Second, differential privacy of the location method was proposed to construct the privacy location, and the cloud service provider used the privacy location to perform the location matching calculation. Third, an improved variant of the Private Set Intersection algorithm was constructed for the case of matching multiple workers for a single task in spatial crowdsourcing, which could efficiently perform location matching while protecting location privacy. To further reduce the computational overhead, Bloom filters were utilized to filter out a large number of free workers with zero location relevance before location matching. The experimental results showed that the scheme was robust and practical in the case of a large number of workers and concurrent task requests.