A location privacy protection method in spatial crowdsourcing

Spatial crowdsourcing is widely used in our daily life, via applications such as DiDi, Uber. With the popularity of smart phone, this paradigm will be more and more popular. However, the popularity of crowdsourcing has increased concerns about the user's privacy. Without adequate privacy protection, no one will accept the task of crowdsourcing. To address the problem above, a new location privacy protection method is proposed in this paper. The method proposed in this paper can not only protect the user's location privacy, but also protect the crowdsourcing task's location privacy. Compared with others, the success rate of task allocation is higher and the travel distance of crowdsourcing workers is shorter. First of all, the coordinates of the worker's location are converted to polar coordinates, and the differential privacy transformation is performed on the location record of polar coordinates. Less noise is added to the polar radius, and more noise is added to the polar angle, which can improve the utility of the sanitized dataset. Finally, the crowdsourcing server allocates the tasks to the crowdsourcing workers according to the sanitized dataset. Experiments are conducted on two real-world datasets to verify its performance. The experimental results show that this method has the advantage of less information loss.

Automated summary

This paper proposes a new method for protecting location privacy, which can protect both the user's and crowdsourcing task's location privacy. Compared with other methods, this method has a higher success rate of task allocation and shorter travel distance for crowdsourcing workers. By converting coordinates to polar coordinates and performing differential privacy transformation, the utility of the sanitized dataset is improved.