Location privacy protection in vehicle-based spatial crowdsourcing via geo-indistinguishability

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, vehicles have been increasingly adopted as participants in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location information to servers to facilitate the utilities of SC services. Traditional location privacy protection mechanisms cannot be directly applied to vehicle-based SC since they assume workers' location on a 2-dimensional plane, which does not take into account the features of vehicle workers' mobility in vehicle road networks. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) in vehicle road networks. Our objective is to design a location obfuscation strategy to minimize the loss of quality-of-service (QoS) due to task distribution with location obfuscation, while guaranteeing geo-indistinguishability to be satisfied. Considering the computational complexity of the VLP problem, by resorting to discretization, we approximate VLP to a linear programming problem that can be solved by existing well-developed approaches (such as the simplex method). To further improve the time efficiency, we reduce the number of constraints in VLP by exploiting key features of geo-indistinguishability in vechicle road networks (such as transitivity). Finally, our experimental results demonstrate that our approach can achieve a reasonable approximation of the minimum QoS loss with location privacy protected, and also outperforms a known state-of-the-art location obfuscation strategy in terms of both QoS and privacy.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1061-1071
Number of pages11
ISBN (Electronic)9781728125190
DOIs
StatePublished - Jul 2019
Event39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 - Richardson, United States
Duration: Jul 7 2019Jul 9 2019

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2019-July

Conference

Conference39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
CountryUnited States
CityRichardson
Period7/7/197/9/19

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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  • Cite this

    Squicciarini, A. C., & Qiu, C. (2019). Location privacy protection in vehicle-based spatial crowdsourcing via geo-indistinguishability. In Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 (pp. 1061-1071). [8885076] (Proceedings - International Conference on Distributed Computing Systems; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCS.2019.00109