Privacy-Preserving Task Assignment in Spatial Crowdsourcing

An Liu, Zhi-Xu Li, Guan-Feng Liu, Kai Zheng, Min Zhang, Qing Li, Xiangliang Zhang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)905-918
Number of pages14
JournalJournal of Computer Science and Technology
Volume32
Issue number5
DOIs
StatePublished - Sep 20 2017

Fingerprint

Dive into the research topics of 'Privacy-Preserving Task Assignment in Spatial Crowdsourcing'. Together they form a unique fingerprint.

Cite this