Geo-obfuscation Mechanisms for Spatial Crowdsourcing via Multi-Objective Evolutionary Optimization

The rapid development of mobile Internet and sharing economy brings the prosperity of spatial crowdsourcing. Spatial crowdsourcing (SC) applications assign various tasks based on workers'location information. However, SC-servers are often untrustworthy and the exposure of users'locations raises privacy concerns. In this paper, we design a framework Geo-MOEA (Multi-Objective Evolutionary Algorithm with Geo-obfuscation) to protect location privacy of workers involved on SC platform in mobile networks environment. We propose an adaptive regionalized obfuscation approach with inference error bounds based on geo-indistinguishability (a strong notion of differential privacy), which is suitable for the context of large-scale location data and task allocations. This enables each worker to report a pseudo-location that is adaptively generated with a personalized inference error threshold. Moreover, we introduce MOEA to optimize the trade-off between SC service availability and privacy protection while confirming theoretically the most general condition on protection location sets for larger search space. Finally, the experimental results on two public datasets show that our Geo-MOEA approach achieves up to 20% reduction in service quality loss while guaranteeing differential and geo-distortion location privacy.