On Sharing Models Instead of Data using Mimic learning for Smart Health Applications

TL;DR

This paper proposes a method for sharing medical models instead of sensitive patient data by using mimic learning, enabling privacy-preserving health predictions with comparable accuracy.

Contribution

It introduces a mimic learning framework for healthcare applications that preserves patient privacy while maintaining model performance.

Findings

Student models achieve similar accuracy to teacher models.

The approach complies with privacy laws by avoiding data sharing.

Effective across multiple medical datasets.

Abstract

Electronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach. The idea is first to train a model on the original sensitive data, called the teacher model. Then, using this model, we can transfer its knowledge to another model, called the student model, without the need to learn the original data used in training the teacher model. The student model is then shared to the public and can be used to make accurate predictions. To assess the mimic learning approach, we have evaluated our scheme using different medical datasets. The results indicate that the student model mimics the teacher model performance in terms of prediction accuracy without the need to access to the patients' original data records.