Key Method for Privacy Protection of Trajectory Data

View Sample PDF

Author(s): Xiang Gong (Hebei University of Environmental Engineering, China), Qiaoqiao Wang (Hebei University of Environmental Engineering, China), Guojie Li (Hebei University of Environmental Engineering, China)and Dehan Kong (Hebei University of Environmental Engineering, China)
Copyright: 2026
Volume: 20
Issue: 1
Pages: 23
Source title: International Journal of Information Security and Privacy (IJISP)
Editor(s)-in-Chief: Yassine Maleh (Sultan Moulay Slimane University, Morocco)and Ahmed A. Abd El-Latif (Menoufia University, Egypt)
DOI: 10.4018/IJISP.404386

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

Purchase

View Key Method for Privacy Protection of Trajectory Data on the publisher's website for pricing and purchasing information.

Abstract

With the extensive proliferation of location-based services, protecting user trajectory privacy against continuous query attacks has become a critical challenge. Existing protection mechanisms often suffer from a rigid trade-off between privacy strength and service quality. To bridge this gap, this study proposes a unified demand-aware trajectory privacy protection framework. First, a fake trajectory generation algorithm is developed that ensures to resist advanced inference attacks. Second, a maximizing demand request algorithm is introduced to resolve conflicts between privacy demands and sparse historical data. Finally, two anonymous zone minimization strategies are implemented. Experimental results using real-world mobility generators demonstrate that the proposed framework improves the anonymous service success rate by more than 13% over baseline location privacy-preserving algorithms while maintaining a smaller anonymous area, balancing privacy and utility

The IRMA Community

Research IRM

Key Method for Privacy Protection of Trajectory Data

Purchase

Abstract

Related Content

IRMA Sponsors