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The rapid development of mobile Internet and sharing economy brings theprosperity of Spatial Crowdsourcing (SC). SC applications assign various tasksaccording to reported location information of task's requesters and outsourcedworkers (such as DiDi, MeiTuan and Uber). However, SC-servers are oftenuntrustworthy and the exposure of users' locations raises privacy concerns. Inthis paper, we design a framework called Geo-MOEA (Multi-Objective EvolutionaryAlgorithm with Geo-obfuscation) to protect location privacy of workers involvedon SC platform in mobile networks environment. We propose an adaptiveregionalized obfuscation approach with inference error bounds based ongeo-indistinguishability (a strong notion of differential privacy), which issuitable for the context of large-scale location data and task allocations.This enables each worker to report a pseudo-location that is adaptivelygenerated with a personalized inference error threshold. Moreover, as a popularcomputational intelligence method, MOEA is introduced to optimize the trade-offbetween SC service availability and privacy protection while ensuringtheoretically the most general condition on protection location sets for largersearch space. Finally, the experimental results on two public datasets showthat our Geo-MOEA approach achieves up to 20% reduction in service quality losswhile guaranteeing differential and geo-distortion location privacy.

Geo-MOEA: A Multi-Objective Evolutionary Algorithm with Geo-obfuscation for Mobile Crowdsourcing Workers