ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets

TL;DR

This study uses transparent machine learning and SHAP analysis to assess and explain the risks of individual re-identification in real-world ECG datasets, highlighting privacy concerns and guiding privacy-preserving strategies.

Contribution

It introduces an explainability-focused approach using SHAP to analyze ECG re-identification risks across diverse datasets, providing insights for privacy enhancement.

Findings

Re-identification accuracy: 76% for gender, 67% for age, 82% for participant ID.

Diverse ECG features contribute differently to re-identification risks.

Highlights need for robust privacy measures in clinical ECG data.

Abstract

While ECG data is crucial for diagnosing and monitoring heart conditions, it also contains unique biometric information that poses significant privacy risks. Existing ECG re-identification studies rely on exhaustive analysis of numerous deep learning features, confining to ad-hoc explainability towards clinicians decision making. In this work, we delve into explainability of ECG re-identification risks using transparent machine learning models. We use SHapley Additive exPlanations (SHAP) analysis to identify and explain the key features contributing to re-identification risks. We conduct an empirical analysis of identity re-identification risks using ECG data from five diverse real-world datasets, encompassing 223 participants. By employing transparent machine learning models, we reveal the diversity among different ECG features in contributing towards re-identification of individuals with an accuracy of 0.76 for gender, 0.67 for age group, and 0.82 for participant ID re-identification. Our approach provides valuable insights for clinical experts and guides the development of effective privacy-preserving mechanisms. Further, our findings emphasize the necessity for robust privacy measures in real-world health applications and offer detailed, actionable insights for enhancing data anonymization techniques.