  Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing
in the era of mobile Internet and sharing economy. It requires workers to
arrive at a particular location for task fulfillment. Effective protection of
location privacy is essential for workers' enthusiasm and valid task
assignment. However, existing SC models with differential privacy usually
perturb real-time location data for both partition and data publication. Such a
way may produce large perturbations to counting queries that affect assignment
success rate and allocation accuracy. This paper proposes a framework (R-HT)
for protecting location privacy of workers taking advantage of both real-time
and historical data. We simulate locations by sampling the probability
distribution learned from historical data, use them for grid partition, and
then publish real-time data under this partitioning with differential privacy.
This realizes that most privacy budget is allocated to the worker count of each
cell and yields an improved Private Spatial Decomposition approach. Moreover,
we introduce some strategies for geocast region construction, including quality
scoring function and local maximum geocast radius. A series of experimental
results on real-world datasets shows that R-HT attains a stable success rate of
task assignment, saves performance overhead and fits for dynamic assignment on
crowdsourcing platforms.
