Privacy Protection in Trajectory Data Publication Based on Differential Privacy

The proliferation of location-aware devices has led to a wide-ranging applicability of trajectory data in diverse real-world scenarios. Despite the prevalent use of the k-means algorithm and differential privacy techniques in mainstream research for the generalization and protection of privacy-sensitive data, limitations persist in effectively reducing noise errors and enhancing overall algorithmic efficiency. This paper addresses the deficiencies observed in existing clustering methodologies applied to user trajectory data, focusing on data privacy preservation and enhanced utility. To this end, we propose a novel incremental clustering framework based on personalized differential privacy. The framework employs dynamic time warping for similarity assessment and incorporates temporal-based cluster protection mechanisms to fulfill privacy requirements. Moreover, it augments the Geo Indistinguishability (GI) privacy protection mechanism to tailor personalized privacy budgets. Subsequently, learning vector quantization is employed for incremental clustering synthesis of trajectory data, completing trajectory publication. Through experimental validation, our proposed model demonstrates significant improvements, with the data usability metric HD increasing by a minimum of 22% compared to existing algorithms, the privacy protection metric AMI improving by at least 18%, and the algorithm’s efficiency enhancing by no less than 20%.