Abstract
Spatial crowdsourcing assigns location-related tasks to a group of workers (people equipped with smart devices and willing to complete the tasks), who complete the tasks according to their scope of work. Since space crowdsourcing usually requires workers’ location information to be uploaded to the crowdsourcing server, it inevitably causes the privacy disclosure of workers. At the same time, it is difficult to allocate tasks effectively in space crowdsourcing. Therefore, in order to improve the task allocation efficiency of spatial crowdsourcing in the case of large task quantity and improve the degree of privacy protection for workers, a new algorithm is proposed in this paper, which can improve the efficiency of task allocation by disturbing the location of workers and task requesters through k-anonymity. Experiments show that the algorithm can improve the efficiency of task allocation effectively, reduce the task waiting time, improve the privacy of workers and task location, and improve the efficiency of space crowdsourcing service when facing a large quantity of tasks.
Highlights
With the increasing use of various mobile devices, the data collected and shared by smartphones have grown exponentially
We introduce a method for the high density of task requester in space crowdsourcing, Dual Privacy preserving (DPP). is method can reduce the waiting time of passengers by setting thresholds, while protecting the location information of taxis through K-anonymity
It can be seen from the figure that the time taken by the calculation increases with the increase in the number of taxis. e DPP algorithm takes more time, which is because the DPP algorithm anonymizes the taxi location K compared with Gedik when sending the taxi location, which results in more computing time spent by the whole system
Summary
With the increasing use of various mobile devices, the data collected and shared by smartphones have grown exponentially. E functions of these three components are as follows Task requesters publish spatio-temporal tasks that are assigned to workers by the crowdsourcing platform, and these tasks contain the location information of the task requesters and the deadline to complete the tasks. Workers will send their temporal and spatial information to the crowdsourcing platform, such as location information and work scope. In the WST mode, the SC server publishes space tasks online, and the worker can arbitrarily select tasks in their vicinity without sending their specific location to the server. When the assignment is successful, the taxi goes to the passenger location to complete the task In this process, the location information obtained by the crowd-sourcing server is all disturbed, and the attacker cannot obtain the detailed location information of taxi and passenger, so as to protect the location information of taxi and passenger
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