Privacy-Preserving ECG Data Analysis with Differential Privacy: A Literature Review and A Case Study

TL;DR

This paper reviews the application of differential privacy to ECG data analysis, discusses practical implementation challenges, and provides a case study demonstrating a six-step process for privacy-preserving query release.

Contribution

It offers a comprehensive overview of differential privacy concepts applied to ECG analysis and presents a practical case study with guidelines for implementation.

Findings

Guidelines for selecting privacy parameters like epsilon.

A six-step process for differentially private ECG query release.

Discussion of challenges in applying differential privacy to ECG data.

Abstract

Differential privacy has become the preeminent technique to protect the privacy of individuals in a database while allowing useful results from data analysis to be shared. Notably, it guarantees the amount of privacy loss in the worst-case scenario. Although many theoretical research papers have been published, practical real-life application of differential privacy demands estimating several important parameters without any clear solutions or guidelines. In the first part of the paper, we provide an overview of key concepts in differential privacy, followed by a literature review and discussion of its application to ECG analysis. In the second part of the paper, we explore how to implement differentially private query release on an arrhythmia database using a six-step process. We provide guidelines and discuss the related literature for all the steps involved, such as selection of the $\epsilon$ value, distribution of the total $\epsilon$ budget across the queries, and estimation of the sensitivity for the query functions. At the end, we discuss the shortcomings and challenges of applying differential privacy to ECG datasets.