A Privacy-Reserving Framework For Spatial Crowdsourcing

Crowdsourcing allows people to distribute tasks to uncertain workers through a platform.Spatial crowdsourcing(SC)is a kind of Crowdsourcing that the tasks contain geometry information.SC task usually contains a target location that requires the worker to travel there and completes the task.Traditional SC platform requires workers and task requesters upload their locations to calculate the distances and complete the assignment.Nowadays privacy becomes more and more important.To preserve privacy,the location should not be disclosed to any untrustworthy entities(even the SC platform).Some previous solutions to preserve workers' location privacy require an online trusty third party(TTP),which is not practical in reality.Other solutions usually suffer from low efficiency or low assignment quality.In this paper,we design a framework to solve these problems.Our contributions include:· We designed a Grid-based framework that allows the SC platform to assign tasks to nearest workers in an online manner without knowing their actual locations.· We introduce an indexing method to optimize the assignment process,which would greatly reduce the overhead caused by the encryption method we use.We verify our method on real-world datasets and experimental results show that our method is efficient,effective and practical.