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Updated: May 9, 2025

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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A location semantic privacy protection model based on spatial influence.

Linghong Kuang1,2, Wenlong Shi1,2, Xueqi Chen1,2

  • 1School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, China.

Scientific Reports
|April 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Location Semantic Privacy Protection Model based on Spatial Influence (LSPPM-SI) to safeguard user trajectory data. The model enhances data availability and semantic protection against sophisticated privacy attacks.

Keywords:
K-anonymityDiversified semanticDummy trajectory synthesisSemantic privacySpatial influence

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Area of Science:

  • Computer Science
  • Data Privacy
  • Geographic Information Systems

Background:

  • Location-based services generate vast trajectory data, valuable for insights but posing privacy risks.
  • Existing privacy methods often neglect location semantics and point-of-interest influence, leaving trajectories vulnerable.
  • Semantic attacks exploit trajectory data, necessitating advanced privacy protection models.

Purpose of the Study:

  • To propose a Location Semantic Privacy Protection Model based on Spatial Influence (LSPPM-SI) resistant to semantic attacks.
  • To enhance the availability and semantic protection of traffic trajectory data.
  • To address the limitations of existing k-anonymity set construction methods.

Main Methods:

  • Developed a location semantic mining algorithm to classify stopovers by positional semantics.
  • Introduced a diversified semantic dummy location selection algorithm and utilized Hilbert curves for efficient anonymity.
  • Defined spatial influence of points of interest to validate dummy trajectories and employed the Kuhn Munkres algorithm for optimal k-anonymity set generation.

Main Results:

  • The proposed LSPPM-SI model significantly improves traffic trajectory data availability by 14%.
  • Achieved a 46.5% enhancement in semantic protection performance compared to traditional models.
  • Effectively prevents attackers from identifying dummy trajectories through spatial influence verification.

Conclusions:

  • LSPPM-SI offers a robust solution for protecting location semantics in trajectory data.
  • The model balances data utility with strong privacy guarantees against advanced attacks.
  • This approach advances the field of privacy-preserving data mining for location-based services.