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.

中文翻译：

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空间众包中的双重隐私保护算法

空间众包将与位置相关的任务分配给一组工人（配备智能设备并愿意完成任务的人员），他们根据工作范围完成任务。由于空间众包通常需要将工人的位置信息上载到众包服务器，因此不可避免地导致了工人的隐私泄露。同时，在空间众包中很难有效地分配任务。因此，为了在任务量大的情况下提高空间众包的任务分配效率，提高对工作人员的隐私保护程度，提出了一种新的算法，该算法可以通过扰乱用户资源来提高任务分配效率。通过k确定工人和任务请求者的位置-匿名。实验表明，该算法可以有效地提高任务分配效率，减少任务等待时间，提高工作人员的隐私和任务位置，并在面对大量任务时提高空间众包服务的效率。