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Related Concept Videos

Ethical Standards II01:23

Ethical Standards II

Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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Introduction to Epidemiology01:26

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Related Experiment Video

Updated: May 23, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Protecting privacy of shared epidemiologic data without compromising analysis potential.

John Cologne1, Eric J Grant, Eiji Nakashima

  • 1Department of Statistics, Radiation Effects Research Foundation, 5-2 Hijiyama Park, Minami-ku, Hiroshima 732-0815, Japan. jcologne@rerf.jp

Journal of Environmental and Public Health
|April 17, 2012
PubMed
Summary

Rounding epidemiologic data effectively balances privacy and utility for researchers. This method reduces identification risk without significantly impacting analysis potential, making data sharing more secure.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Data Privacy

Background:

  • Sharing epidemiologic data requires masking to protect subject privacy, but overmasking can reduce data utility.
  • Statistical disclosure control methods may be too complex for small research collaborations.

Purpose of the Study:

  • To investigate a simple data masking approach for epidemiologic data sharing.
  • To evaluate methods balancing disclosure risk and analytical utility.

Main Methods:

  • Assessed masking techniques including rounding, grouping, and adding random noise.
  • Evaluated disclosure risk and analytical utility using data from the Japanese Atomic-bomb Survivor population.

Main Results:

  • Modest rounding inadequately enhanced security.
  • Rounding to remove several digits of relative accuracy effectively reduced identification risk with minimal impact on utility.
  • Grouping and random noise addition introduced noticeable bias.

Conclusions:

  • Rounding is recommended for masking epidemiologic data when sharing.
  • Masking strategies should be tailored to individual situations, considering disclosure risks and analysis needs.