Differential privacy location data release based on quadtree in mobile edge computing

Abstract

AbstractTo preserve user privacy in the release of location data, some researchers have proposed a distributed data release framework and a location data release algorithm based on mobile edge computing. This framework can preserve location privacy even if location‐based service providers are not trusted. However, the proposed algorithm fails to balance the noise error and the uniform hypothesis error and fails to take the query consistency constraint into account, so query accuracy needs to be improved. Therefore, we follow this framework and propose a differential privacy quadtree partitioning algorithm based on data uniformity heuristic adjustment to further improve query accuracy. First, a differential privacy complete quadtree is constructed, and then the quadtree is heuristically adjusted based on the predefined noise count threshold and the data uniformity threshold to balance the two types of error. Finally, query consistency processing is performed to further improve query accuracy. Experiments based on real‐world datasets are conducted to study the impact of two thresholds on query accuracy. Compared with the previous quadtree‐based differential privacy partitioning algorithm, our proposed algorithm has higher query accuracy while preserving location privacy. In addition, to address the problem of location privacy leakage in the query process, a range counting query framework based on the Hilbert curve is proposed to preserve location privacy in the query process.