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Related Experiment Videos

Privacy-preserving heterogeneous health data sharing.

Noman Mohammed¹, Xiaoqian Jiang, Rui Chen

¹Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada. no_moham@cse.concordia.ca

Journal of the American Medical Informatics Association : JAMIA

|December 18, 2012

Summary

This study introduces a novel algorithm for privacy-preserving healthcare data disclosure, effectively handling both relational and set-valued data using differential privacy. The method generalizes data before adding noise, improving classification analysis performance.

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Area of Science:

Computer Science
Data Privacy
Health Informatics

Background:

Privacy-preserving data publishing is crucial for mining sensitive information.
Epsilon-differential privacy offers robust privacy guarantees without adversary assumptions.
Existing methods address relational and set-valued data separately.

Purpose of the Study:

To propose a unified algorithm for differentially private disclosure of healthcare data containing both relational and set-valued attributes.
To enhance the utility of disclosed data for analytical tasks.

Main Methods:

A novel algorithm that generalizes raw data probabilistically before adding noise.
Ensures epsilon-differential privacy for the disclosed dataset.
Integrates handling of both relational and set-valued data.

Main Results:

The disclosed data effectively supports decision tree induction classifiers.
The proposed algorithm demonstrates scalability and superior performance in classification analysis compared to existing solutions.
Retains essential information for discriminative analysis.

Conclusions:

The algorithm enables private disclosure of complex healthcare data, combining relational and set-valued types.
Maintains data utility for analytical purposes, outperforming prior methods.
Potential utility degradation for very large health databases is noted as a limitation.