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Re-Identification Risk versus Data Utility for Aggregated Mobility Research Using Mobile Phone Location Data.

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Area of Science:

  • Human Mobility Research
  • Data Privacy
  • Spatio-temporal Analysis

Background:

  • Mobile phone location data offers significant potential for human mobility research.
  • High re-identification risks exist due to unique user activity patterns.
  • Privacy protection methods often degrade data utility for analysis.

Purpose of the Study:

  • To assess re-identification risks in Chinese mobile users.
  • To quantify the relationship between re-identification risk and data utility.
  • To establish a privacy-utility tradeoff benchmark for trajectory data.

Main Methods:

  • Applied top N locations and spatio-temporal point attack models to evaluate re-identification risks in Shenzhen.
  • Implemented a spatial generalization approach for privacy protection.
  • Utilized spatially aggregated analysis to assess data utility loss.

Main Results:

  • Re-identification risks in Shenzhen differ from Western countries, demonstrating spatial heterogeneity.
  • A mathematical relationship (y = -axb+c) was found between re-identification risk (x) and data utility (y).
  • The exponent 'b' in the relationship correlates with the attacker's background knowledge.

Conclusions:

  • The study provides crucial insights into the spatial heterogeneity of re-identification risks.
  • The established mathematical model guides data publishers in balancing privacy and utility.
  • This research offers a benchmark for enhancing privacy protection in shared trajectory data.