Protecting location privacy in spatial crowdsourcing using encrypted data

Publication Type:
Conference Proceeding
Citation:
Advances in Database Technology - EDBT, 2017, 2017-March pp. 478 - 481
Issue Date:
2017-01-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
2017_EDBT_privacy.pdfPublished version359.7 kB
Adobe PDF
© 2017, Copyright is with the authors. In spatial crowdsourcing, spatial tasks are outsourced to a set of workers in proximity of the task locations for efficient assignment. It usually requires workers to disclose their locations, which inevitably raises security concerns about the privacy of the workers’ locations. In this paper, we propose a secure SC framework based on encryption, which ensures that workers’ location information is never released to any party, yet the system can still assign tasks to workers situated in proximity of each task’s location. We solve the challenge of assigning tasks based on encrypted data using homomorphic encryption. Moreover, to overcome the efficiency issue, we propose a novel secure indexing technique with a newly devised SKD-tree to index encrypted worker locations. Experiments on real-world data evaluate various aspects of the performance of the proposed SC platform.
Please use this identifier to cite or link to this item: