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Auteur An Liu |
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Efficient task assignment in spatial crowdsourcing with worker and task privacy protection / An Liu in Geoinformatica, vol 22 n° 2 (April 2018)
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Titre : Efficient task assignment in spatial crowdsourcing with worker and task privacy protection Type de document : Article/Communication Auteurs : An Liu, Auteur ; Weiqi Wang, Auteur ; Shuo Shang, Auteur ; Qing Li, Auteur ; Xiangliang Zhang, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] cryptage
[Termes IGN] données localisées des bénévoles
[Termes IGN] géolocalisation
[Termes IGN] production participative
[Termes IGN] protection de la vie privéeRésumé : (Auteur) 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. Numéro de notice : A2018-367 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-017-0305-2 En ligne : https://doi.org/10.1007/s10707-017-0305-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90732
in Geoinformatica > vol 22 n° 2 (April 2018)[article]