A Trajectory K-Anonymity Model Based on Point Density and Partition

TL;DR

This paper introduces a new trajectory K-anonymity model that enhances privacy protection in spatiotemporal datasets by improving data utility and reducing processing time through innovative partitioning and clustering techniques.

Contribution

The paper proposes a novel trajectory K-anonymity model based on Point Density and Partition, improving data utility and efficiency over existing methods.

Findings

Higher data utility in anonymized datasets

Shorter algorithm execution times

Effective resistance to re-identification attacks

Abstract

As people's daily life becomes increasingly inseparable from various mobile electronic devices, relevant service application platforms and network operators can collect numerous individual information easily. When releasing these data for scientific research or commercial purposes, users' privacy will be in danger, especially in the publication of spatiotemporal trajectory datasets. Therefore, to avoid the leakage of users' privacy, it is necessary to anonymize the data before they are released. However, more than simply removing the unique identifiers of individuals is needed to protect the trajectory privacy, because some attackers may infer the identity of users by the connection with other databases. Much work has been devoted to merging multiple trajectories to avoid re-identification, but these solutions always require sacrificing data quality to achieve the anonymity requirement. In order to provide sufficient privacy protection for users' trajectory datasets, this paper develops a study on trajectory privacy against re-identification attacks, proposing a trajectory K-anonymity model based on Point Density and Partition (KPDP). Our approach improves the existing trajectory generalization anonymization techniques regarding trajectory set partition preprocessing and trajectory clustering algorithms. It successfully resists re-identification attacks and reduces the data utility loss of the k-anonymized dataset. A series of experiments on a real-world dataset show that the proposed model has significant advantages in terms of higher data utility and shorter algorithm execution time than other existing techniques.