A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing

By Hien To, Gabriel Ghinita, Cyrus Shahabi. Proceedings of the VLDB Endowment, 2014

370 citations recorded by the source corpus.

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Private spatial data aggregation in the local setting (2016, background): “To et al. [13] study the problem of private spatial decomposition in the context of spatial crowdsourcing.”
Protecting Locations with Differential Privacy under Temporal Correlations (2014, background): “Several recent works have applied diﬀer-ential privacy to publish aggregate information from a large volume of location, trajectory or spatiotemporal data (e.g. [5, 10, 19, 27, 46, 49, 51, 53]).”
A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing (2018, background): “In [61], a scheme to support location privacy preservation was proposed.”
Online mobile Micro-Task Allocation in spatial crowdsourcing (2016, background): “[5], [23] focus on protecting the location privacy of crowd workers in the assignment process.”
A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical Systems (2020, background): “Differential privacy is also utilized in crowdsensing to conceal the sensitive information for given types of applications [30], [31].”