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A standardised differential privacy framework for epidemiological modeling with mobile phone data.

Merveille Koissi Savi1, Akash Yadav2, Wanrong Zhang3

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Differential privacy protects individual privacy in mobile phone mobility data for public health. This method maintains accuracy for epidemiological metrics, ensuring reliable population-level insights without compromising anonymity.

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • Mobile phone data are crucial for tracking human mobility during pandemics like COVID-19.
  • Protecting individual privacy in location data is essential for ethical public policy and epidemiological modeling.
  • Traditional data aggregation methods fail to guarantee anonymity, necessitating advanced privacy-preserving techniques.

Purpose of the Study:

  • To evaluate differential privacy as an anonymization tool for mobile phone mobility data in epidemiological models.
  • To assess the impact of statistically quantified noise on common epidemiological metrics.
  • To determine the robustness of differential privacy for public health applications.

Main Methods:

  • Applied differential privacy by adding statistical noise to mobile phone location data.
  • Analyzed the bias introduced to ten common epidemiological metrics.
  • Quantified the relationship between noise levels and metric accuracy using a count transition matrix.
  • Developed a modular software pipeline for reproducibility.

Main Results:

  • Many epidemiological metrics remained accurate, close to non-private values, with noise levels below 20 (ϵ = 0.05 per release).
  • Differential privacy demonstrated a statistically verifiable protection against identifiability.
  • The study confirmed the utility of privacy-preserving mobility data for public health.

Conclusions:

  • Differential privacy offers a robust solution for anonymizing mobility data while retaining valuable epidemiological insights.
  • This approach balances individual privacy with the need for population-level data in public health.
  • The developed framework supports further research and application of privacy-preserving epidemiological modeling.