Open Access

ARTICLE

Design and Application of a New Distributed Dynamic Spatio-Temporal Privacy Preserving Mechanisms

Jiacheng Xiong¹, Xingshu Chen^1,2,3,*, Xiao Lan^2,3, Liangguo Chen^1,2

1 School of Cyber Science and Engineering, Sichuan University, Chengdu, 610065, China
2 Key Laboratory of Data Protection and Intelligent Management (Sichuan University), Ministry of Education, Chengdu, 610065, China
3 Cyber Science Research Institute, Sichuan University, Chengdu, 610065, China

* Corresponding Author: Xingshu Chen. Email:

Computers, Materials & Continua 2025, 84(2), 2273-2303. https://doi.org/10.32604/cmc.2025.063984

Received 31 January 2025; Accepted 29 April 2025; Issue published 03 July 2025

Abstract

In the era of big data, the growing number of real-time data streams often contains a lot of sensitive privacy information. Releasing or sharing this data directly without processing will lead to serious privacy information leakage. This poses a great challenge to conventional privacy protection mechanisms (CPPM). The existing data partitioning methods ignore the number of data replications and information exchanges, resulting in complex distance calculations and inefficient indexing for high-dimensional data. Therefore, CPPM often fails to meet the stringent requirements of efficiency and reliability, especially in dynamic spatiotemporal environments. Addressing this concern, we proposed the Principal Component Enhanced Vantage-point tree (PEV-Tree), which is an enhanced data structure based on the idea of dimension reduction, and constructed a Distributed Spatio-Temporal Privacy Preservation Mechanism (DST-PPM) on it. In this work, principal component analysis and the vantage tree are used to establish the PEV-Tree. In addition, we designed three distributed anonymization algorithms for data streams. These algorithms are named CK-AA, CL-DA, and CT-CA, fulfill the anonymization rules of K-Anonymity, L-Diversity, and T-Closeness, respectively, which have different computational complexities and reliabilities. The higher the complexity, the lower the risk of privacy leakage. DST-PPM can reduce the dimension of high-dimensional information while preserving data characteristics and dividing the data space into vantage points based on distance. It effectively enhances the data processing workflow and increases algorithm efficiency. To verify the validity of the method in this paper, we conducted empirical tests of CK-AA, CL-DA, and CT-CA on conventional datasets and the PEV-Tree, respectively. Based on the big data background of the Internet of Vehicles, we conducted experiments using artificial simulated on-board network data. The results demonstrated that the operational efficiency of the CK-AA, CL-DA, and CT-CA is enhanced by 15.12%, 24.55%, and 52.74%, respectively, when deployed on the PEV-Tree. Simultaneously, during homogeneity attacks, the probabilities of information leakage were reduced by 2.31%, 1.76%, and 0.19%, respectively. Furthermore, these algorithms showcased superior utility (scalability) when executed across PEV-Trees of varying scales in comparison to their performance on conventional data structures. It indicates that DST-PPM offers marked advantages over CPPM in terms of efficiency, reliability, and scalability.

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Keywords

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