Privacy-Preserving Task Assignment in Spatial Crowdsourcing

Handle URI:
http://hdl.handle.net/10754/626013
Title:
Privacy-Preserving Task Assignment in Spatial Crowdsourcing
Authors:
Liu, An; Li, Zhi-Xu; Liu, Guan-Feng; Zheng, Kai; Zhang, Min; Li, Qing; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
With the progress of mobile devices and wireless networks, spatial crowdsourcing (SC) is emerging as a promising approach for problem solving. In SC, spatial tasks are assigned to and performed by a set of human workers. To enable effective task assignment, however, both workers and task requesters are required to disclose their locations to untrusted SC systems. In this paper, we study the problem of assigning workers to tasks in a way that location privacy for both workers and task requesters is preserved. We first combine the Paillier cryptosystem with Yao’s garbled circuits to construct a secure protocol that assigns the nearest worker to a task. Considering that this protocol cannot scale to a large number of workers, we then make use of Geohash, a hierarchical spatial index to design a more efficient protocol that can securely find approximate nearest workers. We theoretically show that these two protocols are secure against semi-honest adversaries. Through extensive experiments on two real-world datasets, we demonstrate the efficiency and effectiveness of our protocols.
KAUST Department:
King Abdullah University of Science and Technology, Thuwal, 23955, , Saudi Arabia
Citation:
Liu A, Li Z-X, Liu G-F, Zheng K, Zhang M, et al. (2017) Privacy-Preserving Task Assignment in Spatial Crowdsourcing. Journal of Computer Science and Technology 32: 905–918. Available: http://dx.doi.org/10.1007/s11390-017-1772-5.
Publisher:
Springer Nature
Journal:
Journal of Computer Science and Technology
Issue Date:
20-Sep-2017
DOI:
10.1007/s11390-017-1772-5
Type:
Article
ISSN:
1000-9000; 1860-4749
Sponsors:
This work was partially supported by King Abdullah University of Science and Technology (KAUST) and the National Natural Science Foundation of China under Grant Nos. 61572336, 61632016, 61402313, 61572335, and 61472337.
Additional Links:
http://link.springer.com/article/10.1007/s11390-017-1772-5
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorLiu, Anen
dc.contributor.authorLi, Zhi-Xuen
dc.contributor.authorLiu, Guan-Fengen
dc.contributor.authorZheng, Kaien
dc.contributor.authorZhang, Minen
dc.contributor.authorLi, Qingen
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2017-10-30T08:39:50Z-
dc.date.available2017-10-30T08:39:50Z-
dc.date.issued2017-09-20en
dc.identifier.citationLiu A, Li Z-X, Liu G-F, Zheng K, Zhang M, et al. (2017) Privacy-Preserving Task Assignment in Spatial Crowdsourcing. Journal of Computer Science and Technology 32: 905–918. Available: http://dx.doi.org/10.1007/s11390-017-1772-5.en
dc.identifier.issn1000-9000en
dc.identifier.issn1860-4749en
dc.identifier.doi10.1007/s11390-017-1772-5en
dc.identifier.urihttp://hdl.handle.net/10754/626013-
dc.description.abstractWith the progress of mobile devices and wireless networks, spatial crowdsourcing (SC) is emerging as a promising approach for problem solving. In SC, spatial tasks are assigned to and performed by a set of human workers. To enable effective task assignment, however, both workers and task requesters are required to disclose their locations to untrusted SC systems. In this paper, we study the problem of assigning workers to tasks in a way that location privacy for both workers and task requesters is preserved. We first combine the Paillier cryptosystem with Yao’s garbled circuits to construct a secure protocol that assigns the nearest worker to a task. Considering that this protocol cannot scale to a large number of workers, we then make use of Geohash, a hierarchical spatial index to design a more efficient protocol that can securely find approximate nearest workers. We theoretically show that these two protocols are secure against semi-honest adversaries. Through extensive experiments on two real-world datasets, we demonstrate the efficiency and effectiveness of our protocols.en
dc.description.sponsorshipThis work was partially supported by King Abdullah University of Science and Technology (KAUST) and the National Natural Science Foundation of China under Grant Nos. 61572336, 61632016, 61402313, 61572335, and 61472337.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/article/10.1007/s11390-017-1772-5en
dc.subjectspatial crowdsourcingen
dc.subjectspatial task assignmenten
dc.subjectlocation privacyen
dc.subjectmutual privacy protectionen
dc.titlePrivacy-Preserving Task Assignment in Spatial Crowdsourcingen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technology, Thuwal, 23955, , Saudi Arabiaen
dc.identifier.journalJournal of Computer Science and Technologyen
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, Suzhou, 215006, , Chinaen
dc.contributor.institutionBeijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, 100872, , Chinaen
dc.contributor.institutionDepartment of Computer Science, City University of Hong Kong, Hong Kong, , Chinaen
kaust.authorLiu, Anen
kaust.authorZhang, Xiangliangen
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