Assessing the Re-Identification Risk in ECG Datasets and an Application of Privacy Preserving Techniques in ECG Analysis

<p>In this work, first we investigate the use of ECG signal as a biometric in human identification systems using deep learning models. We train convolutional neural network models on ECG samples from approximately 81k patients. Our models achieved an over-all accuracy of 95.69%. Further, we assess the accuracy of our ECG identification model for distinct groups of patients with particular heart conditions and combinations of such conditions. For example, we observed that the identification accuracy was the highest (99.7%) for patients with both ST changes and supraventricular tachycardia. On the other hand, we also found that the identification rate was the lowest for patients diagnosed with both atrial fibrillation and complete right bundle branch block (49%).</p> <p>Next, we discuss the implications of our findings from the ECG identification models regarding the re-identification risks for the patients and how seemingly anonymized ECG datasets can cause privacy leakages. For some hypothetical scenarios such as when a patient contributes to two different research datasets, we try to quantify the privacy risks. We estimate the probability of how uniquely and accurately one can re-identify patients with a specific type of heart condition contributing to multiple ECG datasets containing data fields like age, gender, and location. We also discuss the new ECG-based demographics detection technology and how it might compromise patients’ privacy even to a degree where someone can find a patient’s residence solely based on an ECG sample. The implications of our findings for the privacy regulations such as HIPAA or GDPR are discussed as well.</p> <p>In contrast to common traditional belief that statistical aggregate or anonymized databases are safe to share, it can be proven that even aggregation does not guarantee privacy and individuals can be re-identified from the published aggregated results. Differential privacy is a privacy preserving data analysis technique which protects the privacy of individuals’ in a database by adding the right amount of noise to perturbate the results. In the last chapter, we will discuss an end-to-end application of differential privacy to an ECG dataset in order to safely share useful statistics with the public.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year dc:date.available

2021

Author dc:creator

• Ghazarian, Arin

Contributors dc:contributor

• Cyril Rakovski
• Daniel Alpay
• Anthony Chang

• Arrhythmia
• Biometrics
• Convolutional neural networks (CNN)
• ECG Identification
• Deep Learning
• Differential Privacy
• Data Science

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/20

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1020