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Inference Control in a Diabetes Data Set Using a Java-Based Prototype of LDH Algorithm.

Georgios Feretzakis¹, Konstantinos Mitropoulos¹, Dimitris Kalles¹

¹School of Science and Technology, Hellenic Open University, Patras 263 35, Greece; georgios.feretzakis@ac.eap.gr; kmitrop@otenet.gr; kalles@eap.gr; verykios@eap.gr.

Studies in Health Technology and Informatics

|January 22, 2022

Summary

This summary is machine-generated.

The Local Distortion Hiding (LDH) algorithm effectively protects sensitive decision tree (DT) rules in healthcare data sharing. Experiments show LDH controls inference while preserving DT structure and performance metrics.

Keywords:

Inference control data security machine learning privacy-preserving

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Area of Science:

Health Informatics
Data Privacy
Machine Learning

Background:

Healthcare data sharing is increasing, raising concerns about indirect data disclosure via inference channels.
Protecting sensitive decision tree (DT) rules is crucial during data analysis and sharing.

Purpose of the Study:

To evaluate the effectiveness of the Local Distortion Hiding (LDH) algorithm in protecting sensitive DT rules.
To assess LDH's ability to prevent inference and maintain DT performance metrics in a healthcare context.

Main Methods:

Implementation of the LDH algorithm using a Java-based prototype.
Conducting eight experiments on a diabetes dataset to test inference control and DT performance.
Hiding eight terminal nodes within the DT structure.

Main Results:

The LDH algorithm demonstrated satisfactory performance in controlling inference channels.
The structure and performance metrics of the decision trees were successfully maintained after applying LDH.
Experiments confirmed the algorithm's efficacy in a real-world diabetes dataset.

Conclusions:

The LDH algorithm provides a viable solution for enhancing data privacy in healthcare by protecting sensitive decision tree rules.
LDH balances data utility and privacy preservation, making it suitable for secure data sharing applications.