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Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values.

Junhak Lee1, Jinwoo Jeong1, Sungji Jung1

  • 1Department of Industrial Security, Chung-Ang University, Seoul 06974, Korea.

Journal of Personalized Medicine
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a method to verify de-identification techniques for personal healthcare information, ensuring both data confidentiality and usability. It uses machine learning models to select the best pseudonym processing methods for big data applications.

Keywords:
de-identificationexplainable artificial intelligencemachine learningmedical datatree-based method

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Area of Science:

  • Data Science
  • Health Informatics
  • Machine Learning

Background:

  • The increasing use of big data and cloud computing highlights the need for robust pseudonym information management.
  • Current tools for verifying de-identification techniques are insufficient to guarantee both data confidentiality and usability.
  • Personal healthcare information requires stringent de-identification to balance data utility and privacy protection.

Purpose of the Study:

  • To propose and validate a novel methodology for verifying de-identification techniques in personal healthcare information.
  • To ensure that de-identification methods maintain data confidentiality while preserving data usability for analysis.
  • To develop a machine learning-driven process for selecting optimal de-identification strategies.

Main Methods:

  • Generated and preprocessed numerical datasets representing de-identification techniques using statistical, personal, and medical data.
  • Constructed and evaluated five tree-based regression models (Decision Tree, Random Forest, GBM, XGBoost, LightGBM) to identify nonlinear relationships.
  • Applied Shapley Additive Explanation (SHAP), an explainable AI technique, to the best-performing model for policy development.

Main Results:

  • Identified nonlinear relationships between variables crucial for de-identification effectiveness.
  • Selected the most effective machine learning model for pseudonym processing from the evaluated tree-based regression models.
  • Demonstrated the utility of SHAP for interpreting model predictions and informing pseudonym processing policies.

Conclusions:

  • The proposed verification method effectively assesses de-identification techniques for personal healthcare information.
  • Machine learning, particularly tree-based models and explainable AI, can optimize the selection of pseudonym processing methodologies.
  • This approach enhances both data privacy and utility in big data environments, crucial for healthcare applications.