A Personalized Fuzzy Method Based on Local Differential Privacy for Location Data Publication

With the continuous development deepening of the application of location-based services, more and more attention has been paid to the privacy and security protection of location data. In recent years, differential privacy as a rigorous and provable privacy protection technology, has been more and more widely used in location privacy protection, and local differential privacy has attracted special attention due to its independence from trusted data centers. However, the existing location data publishing methods based on local differential privacy still have the following problems, such as the lack of flexible personalized privacy setting method, the excessive noise, and low computational efficiency. In response to these issues, this article proposes a personalized method based on local differential privacy and fuzzy set, which divides the spatial grid according to the privacy needs of users in different locations, limits the candidate set of user publishing locations to the adjacent spatial grid, and perturbs the membership degree on the basis of the fuzzy set. Theoretical analysis and experimental results show that the proposed method has good efficiency and data availability while satisfying the requirements of differential privacy protection effect and flexible personalized privacy setting.