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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Utility-preserving anonymization for health data publishing.

Hyukki Lee¹, Soohyung Kim², Jong Wook Kim³

¹Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

BMC Medical Informatics and Decision Making

|July 12, 2017

Summary

This study introduces a new method for anonymizing electronic health records (EHRs) to protect privacy while preserving data utility. The novel approach significantly reduces information loss compared to existing techniques, improving the accuracy of health data analysis.

Keywords:

Data anonymization K-anonymity Medical privacy Utility-preserving data publishing

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Area of Science:

Health Informatics
Data Privacy
Computer Science

Background:

Publishing electronic health records (EHRs) poses privacy risks due to sensitive information.
Anonymization techniques like generalization are used for privacy-preserving data publishing (PPDP).
Existing methods often result in excessive information loss, compromising data utility.

Purpose of the Study:

To propose a novel utility-preserving anonymization method for PPDP.
To develop an anonymization algorithm that minimizes information loss.
To enhance the utility of anonymized EHRs for analysis.

Main Methods:

A three-part utility-preserving anonymization method: utility-preserving model, counterfeit record insertion, and cataloging.
Application of a full-domain generalization algorithm within the proposed method.
Comparative evaluation against existing methods based on information loss and analysis error rates.

Main Results:

The proposed method demonstrated lower information loss across various quality metrics compared to existing approaches.
Analysis of real-world EHRs showed a minimal error rate between anonymized and original data.
The anonymization algorithm effectively preserved data utility.

Conclusions:

A new utility-preserving anonymization method and algorithm for PPDP were successfully developed.
Experimental results confirm significantly improved EHR utility with the proposed anonymization method.
The approach offers a superior solution for balancing privacy and utility in health data publishing.