Location Privacy in Spatial Crowdsourcing
Hien To, Cyrus Shahabi

TL;DR
This paper reviews location privacy challenges in spatial crowdsourcing, analyzing threats and solutions during task assignment and reporting phases, and discusses open problems and future research directions.
Contribution
It provides a comprehensive overview and comparative analysis of existing privacy-preserving techniques in spatial crowdsourcing, highlighting strengths and limitations.
Findings
Various privacy threats identified for workers and requesters
Comparison of technical approaches like pseudonymity, cloaking, perturbation, encryption
Discussion of open problems and future research directions
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 to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters 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 SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Mobile Crowdsensing and Crowdsourcing · Privacy, Security, and Data Protection
