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Differential privacy protection method based on published trajectory cross-correlation constraint.

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Summary
This summary is machine-generated.

This study introduces a novel personalized differential privacy method for trajectory data. It enhances data utility and privacy protection using cross-correlation constraints and spatio-temporal noise.

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Area of Science:

  • Computer Science
  • Data Privacy
  • Cybersecurity

Background:

  • Low data utilization and privacy concerns hinder the effective use of trajectory data.
  • Existing methods often struggle to balance data utility with robust privacy protection.

Purpose of the Study:

  • To propose a personalized differential privacy method for trajectory data that enhances both data utilization and privacy protection.
  • To develop a privacy model that integrates trajectory sensitivity, user requirements, and privacy budgets.
  • To ensure spatio-temporal correlation in added noise for stronger privacy guarantees.

Main Methods:

  • A personalized differential privacy protection method based on cross-correlation constraints is proposed.
  • An (R,Ɛ)-extended differential privacy protection model is developed, incorporating trajectory sensitivity and privacy budgets.
  • Autocorrelation Laplace transform is used to generate spatio-temporally correlated Gaussian white noise.
  • A cross-correlation constraint mechanism is defined for published trajectory sequences.

Main Results:

  • The proposed method effectively protects sensitive location points while maintaining data utility.
  • Simulation experiments validate the feasibility, effectiveness, and rationality of the algorithm.
  • The method provides strong privacy guarantees against adversaries by ensuring spatio-temporal correlation.

Conclusions:

  • The developed method offers a robust solution for privacy-preserving trajectory data analysis.
  • It successfully balances the trade-off between data utility and differential privacy.
  • The cross-correlation constraint mechanism enhances the security of published trajectory data.