Novel trajectory privacy-preserving method based on prefix tree using differential privacy

Abstract

Location-based services, such as DiDi and bike sharing, are becoming increasingly popular. However, the use of these services raises privacy concerns. In the past few years, differential privacy technology has been applied to location information protection. However, most existing models largely fail to resist complex background-knowledge attacks. This paper proposes a novel privacy preservation method for trajectory data. It is based on a prefix tree and uses differential privacy. It should be noted that existing methods only consider either a certain location point or the entire trajectory. In the proposed prefix tree structure, the nodes of the tree store the trajectory segments. The parameter minimum description length method is combined with the Dijkstra method to select feature trajectory points that represent the entire trajectory, thus further reducing the (computational) complexity of data processing. To protect privacy, Laplacian noise is added to the location data of trajectory segments by using differential privacy. In addition, a background and contextual information attack model is proposed, and the corresponding protection method is provided. Finally, a Markov chain is used to limit the size of the noise added to the data. The proposed algorithm is compared with related state-of-the-art algorithms on a public dataset. The results demonstrate that our algorithm can ensure not only privacy but also data availability.