  The rapid development of mobile Internet and sharing economy brings the
prosperity of Spatial Crowdsourcing (SC). SC applications assign various tasks
according to reported location information of task's requesters and outsourced
workers (such as DiDi, MeiTuan and Uber). However, SC-servers are often
untrustworthy and the exposure of users' locations raises privacy concerns. In
this paper, we design a framework called 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, as a popular
computational intelligence method, MOEA is introduced to optimize the trade-off
between SC service availability and privacy protection while ensuring
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.
