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Key Method for Privacy Protection of Trajectory Data

Xiang Gong et al.

International Journal of Information Security and Privacy2026https://doi.org/10.4018/ijisp.404386article
ABDC C
Weight
0.50

What the paper says

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

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https://doi.org/https://doi.org/10.4018/ijisp.404386

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@article{xiang2026,
  title        = {{Key Method for Privacy Protection of Trajectory Data}},
  author       = {Xiang Gong et al.},
  journal      = {International Journal of Information Security and Privacy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijisp.404386},
}

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Key Method for Privacy Protection of Trajectory Data

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.