Preserving Location Privacy in Spatial Crowdsourcing Under Quality Control

Abstract

Emerging spatial crowdsourcing (SC) provides an approach for collecting and analyzing spatiotemporal information from intelligent transportation systems. However, the exposure of massive location privacy to potential adversaries for the purpose of quality control makes workers more vulnerable. To protect workers' location privacy, an obfuscation scheme is proposed to incorporate uncertainties into the SC quality control problem through obfuscating the standard location data in terms of both space and time. Two measures, location entropy and results accuracy, are used to evaluate the performance of location privacy protection. We theoretically and experimentally confirm the security and accuracy of the obfuscation approach. The results of experiments show that: a) hiding workers' location from the requester reduces the quality of SC; and b) obfuscation arithmetic with appropriate obfuscation coefficients protects workers' location privacy with little effect on SC quality. Under the protection of this obfuscation scheme, the new system provides better security and similar quality compared to the existing SC system.

Keywords

• Spatial crowdsourcing
• obfuscation location privacy of workers
• quality control
• EM algorithm