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Location Privacy in Spatial Crowdsourcing

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Handbook of Mobile Data Privacy

Abstract

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks’ locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted entities (SC server) for effective assignments of tasks to workers.

This chapter first identifies privacy threats toward both workers and tasks during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC server). The latter phase is reporting, in which workers travel to the tasks’ locations, complete the tasks and upload their reports to the server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks).

This chapter aims to provide an overview of the state-of-the-art in protecting users’ location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work.

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Notes

  1. 1.

    Task locations can indirectly reveal requesters’ location, i.e., requesters often post tasks in the proximity of their locations.

  2. 2.

    Exact geographical coordinates of the tasks may not be published; instead, their cloaked locations or representative names are provided.

  3. 3.

    We present the privacy threats to a pull-based SC system only; however, some of these privacy threats also occur in push-based SC such as iRain.

  4. 4.

    We obtained this information via JavaScript code.

  5. 5.

    However, this study assumes that workers trust one another. Hence, a more recent study [35] solves a similar problem as in [12] without the assumption of trusted workers.

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Authors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Hien To & Cyrus Shahabi

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  1. Hien To
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  2. Cyrus Shahabi
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Correspondence to Hien To or Cyrus Shahabi .

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Editors and Affiliations

  1. IBM Watson Health Headquarters, Cambridge, MA, USA

    Aris Gkoulalas-Divanis

  2. Dipartimento di Informatica, University of Milan, Milan, Italy

    Claudio Bettini

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To, H., Shahabi, C. (2018). Location Privacy in Spatial Crowdsourcing. In: Gkoulalas-Divanis, A., Bettini, C. (eds) Handbook of Mobile Data Privacy . Springer, Cham. https://doi.org/10.1007/978-3-319-98161-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-98161-1_7

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