Privacy-Preserving Task Matching With Threshold Similarity Search via Vehicular Crowdsourcing

• Published in: IEEE transactions on vehicular technology Vol. 70; no. 7; pp. 7161 - 7175
• Main Authors: Song, Fuyuan, Qin, Zheng, Liu, Dongxiao, Zhang, Jixin, Lin, Xiaodong, Shen, Xuemin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Crowdsourcing; Encryption; Keyword search; Matching; Privacy; privacy-preserving; Searching; Security; Servers; Similarity; similarity search; Task analysis; task matching; Vehicular crowdsourcing; Workers
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2021.3088869

In vehicular crowdsourcing, task requesters rely on a server to distribute spatial crowdsourcing tasks to on-road vehicular workers based on interests and locations. To protect the privacy of the interests and locations, both requesters and workers prefer to encrypt the information before uploading them to the server. However, such an encryption-before-outsourcing paradigm makes it a challenging issue to conduct the task matching. In this paper, we propose a Privacy-Preserving Task Matching (PPTM) with threshold similarity search via vehicular crowdsourcing. We first propose an interest-based PPTM by transforming vehicular workers' interests into binary vectors. By using Symmetric-key Threshold Predicate Encryption (STPE) and proxy re-encryption, PPTM achieves privacy-preserving multi-keyword task matching with Jaccard similarity search in multi-worker multi-requester setting. Furthermore, by comparing the Euclidean distances between workers and requesters against a pre-defined threshold, PPTM preserves the location privacy of workers and requesters that only reveals the comparison results to the crowdsourcing server. The security analysis and extensive experiments demonstrate that PPTM protects the confidentiality of locations and interests of requesters and workers while achieving the efficient task matching.