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dc.contributor.authorLiu, An
dc.contributor.authorWang, Weiqi
dc.contributor.authorShang, Shuo
dc.contributor.authorLi, Qing
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2017-10-03T12:49:35Z
dc.date.available2017-10-03T12:49:35Z
dc.date.issued2017-08-01
dc.identifier.citationLiu A, Wang W, Shang S, Li Q, Zhang X (2017) Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica. Available: http://dx.doi.org/10.1007/s10707-017-0305-2.
dc.identifier.issn1384-6175
dc.identifier.issn1573-7624
dc.identifier.doi10.1007/s10707-017-0305-2
dc.identifier.urihttp://hdl.handle.net/10754/625718
dc.description.abstractSpatial crowdsourcing (SC) outsources tasks to a set of workers who are required to physically move to specified locations and accomplish tasks. Recently, it is emerging as a promising tool for emergency management, as it enables efficient and cost-effective collection of critical information in emergency such as earthquakes, when search and rescue survivors in potential ares are required. However in current SC systems, task locations and worker locations are all exposed in public without any privacy protection. SC systems if attacked thus have penitential risk of privacy leakage. In this paper, we propose a protocol for protecting the privacy for both workers and task requesters while maintaining the functionality of SC systems. The proposed protocol is built on partially homomorphic encryption schemes, and can efficiently realize complex operations required during task assignment over encrypted data through a well-designed computation strategy. We prove that the proposed protocol is privacy-preserving against semi-honest adversaries. Simulation on two real-world datasets shows that the proposed protocol is more effective than existing solutions and can achieve mutual privacy-preserving with acceptable computation and communication cost.
dc.description.sponsorshipResearch reported in this publication was partially supported by KAUST and Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61632016, 61402313, 61472337), and has been benefited from discussions with Dr. Ke Sun in MINE lab at KAUST.
dc.publisherSpringer Nature
dc.relation.urlhttps://link.springer.com/article/10.1007%2Fs10707-017-0305-2
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s10707-017-0305-2
dc.subjectSpatial crowdsourcing
dc.subjectSpatial task assignment
dc.subjectLocation privacy
dc.subjectMutual privacy protection
dc.titleEfficient task assignment in spatial crowdsourcing with worker and task privacy protection
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalGeoInformatica
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, Suzhou, China
dc.contributor.institutionDepartment of Computer Science, City University of Hong Kong, Hong Kong, China
kaust.personLiu, An
kaust.personShang, Shuo
kaust.personZhang, Xiangliang
refterms.dateFOA2018-08-01T00:00:00Z
dc.date.published-online2017-08-01
dc.date.published-print2018-04


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