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Privacy preserving data visualizations.

Demetris Avraam1,2, Rebecca Wilson1,3, Oliver Butters1,3

  • 1Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, UK.

EPJ Data Science
|January 14, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed anonymization techniques to create privacy-preserving data visualizations. These methods allow for the sharing of sensitive data insights while protecting individual confidentiality in statistical analysis.

Keywords:
AnonymizationData visualizationsDisclosure controlPrivacy protectionSensitive data

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Area of Science:

  • Data Science
  • Statistical Analysis
  • Information Security

Background:

  • Data visualizations are crucial for understanding complex datasets, revealing patterns and relationships hidden in raw data.
  • Sharing sensitive data, common in medicine and social sciences, is restricted by privacy laws and ethical guidelines.
  • Traditional data visualizations risk re-identification of individuals, leading to prohibitions on their use in data sharing.

Purpose of the Study:

  • To propose and evaluate anonymization techniques for generating privacy-preserving data visualizations.
  • To enable the display of descriptive plots and diagnostic plots for research without compromising participant confidentiality.
  • To maintain the statistical properties of the original data in the generated visualizations.

Main Methods:

  • k-anonymization: reducing data granularity through suppression and generalization.
  • Deterministic approach: replacing observations with centroids of k-nearest neighbors.
  • Probabilistic approach: perturbing attributes with random noise.

Main Results:

  • Demonstrated the generation of privacy-preserving visualizations using the proposed anonymization techniques.
  • Applied methods to exploratory data analysis and inferential regression plot diagnostics.
  • Evaluated the strengths and limitations of each anonymization approach for visualization.

Conclusions:

  • Anonymization techniques offer a viable solution for creating informative yet privacy-preserving data visualizations.
  • These methods facilitate the ethical sharing and analysis of sensitive data, overcoming disclosure restrictions.
  • The proposed techniques balance data utility with robust confidentiality protection for research.