Spatial crowdsourcing assigns location-related tasks to a group of workers (people equipped with smart devices and willing to\ncomplete the tasks), who complete the tasks according to their scope of work. Since space crowdsourcing usually requires workersâ??\nlocation information to be uploaded to the crowdsourcing server, it inevitably causes the privacy disclosure of workers. At the\nsame time, it is difficult to allocate tasks effectively in space crowdsourcing. Therefore, in order to improve the task allocation\nefficiency of spatial crowdsourcing in the case of large task quantity and improve the degree of privacy protection for workers, a\nnew algorithm is proposed in this paper, which can improve the efficiency of task allocation by disturbing the location of workers\nand task requesters through k-anonymity. Experiments show that the algorithm can improve the efficiency of task allocation\neffectively, reduce the task waiting time, improve the privacy of workers and task location, and improve the efficiency of space\ncrowdsourcing service when facing a large quantity of tasks.
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