Efficient task assignment in spatial crowdsourcing with worker and task privacy protection

Handle URI:
http://hdl.handle.net/10754/625718
Title:
Efficient task assignment in spatial crowdsourcing with worker and task privacy protection
Authors:
Liu, An; Wang, Weiqi; Shang, Shuo; Li, Qing; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
Spatial 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.
KAUST Department:
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Citation:
Liu 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.
Publisher:
Springer Nature
Journal:
GeoInformatica
Issue Date:
1-Aug-2017
DOI:
10.1007/s10707-017-0305-2
Type:
Article
ISSN:
1384-6175; 1573-7624
Sponsors:
Research 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.
Additional Links:
https://link.springer.com/article/10.1007%2Fs10707-017-0305-2
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorLiu, Anen
dc.contributor.authorWang, Weiqien
dc.contributor.authorShang, Shuoen
dc.contributor.authorLi, Qingen
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2017-10-03T12:49:35Z-
dc.date.available2017-10-03T12:49:35Z-
dc.date.issued2017-08-01en
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.en
dc.identifier.issn1384-6175en
dc.identifier.issn1573-7624en
dc.identifier.doi10.1007/s10707-017-0305-2en
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.en
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.en
dc.publisherSpringer Natureen
dc.relation.urlhttps://link.springer.com/article/10.1007%2Fs10707-017-0305-2en
dc.subjectSpatial crowdsourcingen
dc.subjectSpatial task assignmenten
dc.subjectLocation privacyen
dc.subjectMutual privacy protectionen
dc.titleEfficient task assignment in spatial crowdsourcing with worker and task privacy protectionen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabiaen
dc.identifier.journalGeoInformaticaen
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, Suzhou, Chinaen
dc.contributor.institutionDepartment of Computer Science, City University of Hong Kong, Hong Kong, Chinaen
kaust.authorLiu, Anen
kaust.authorShang, Shuoen
kaust.authorZhang, Xiangliangen
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