# Linkage Attacks Expose Identity Risks in Public ECG Data Sharing

arXiv: https://arxiv.org/abs/2508.15850 · HTML: https://mlsanity.com/paper/2508.15850/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://mlsanity.com/paper/2106.10270/) (2021)
- [HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations](https://mlsanity.com/paper/2409.19487/) (2024)
- [Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection](https://mlsanity.com/paper/2402.10862/) (2024)
- [Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach](https://mlsanity.com/paper/2407.08215/) (2024)
- [ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets](https://mlsanity.com/paper/2408.10228/) (2024)
- [TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis](https://mlsanity.com/paper/2503.13495/) (2025)

## Built on by (most cited first, 4 of 4)

- [CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation](https://mlsanity.com/paper/2604.10420/) (2026)
- [Membership Inference Attacks Expose Participation Privacy in ECG Foundation Encoders](https://mlsanity.com/paper/2604.10424/) (2026)
- [Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices](https://mlsanity.com/paper/2606.11556/) (2026)
- [REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality](https://mlsanity.com/paper/2607.06037/) (2026)

## Abstract (arXiv)

The increasing availability of publicly shared electrocardiogram (ECG) data raises critical privacy concerns, as its biometric properties make individuals vulnerable to linkage attacks. Unlike prior studies that assume idealized adversarial capabilities, we evaluate ECG privacy risks under realistic conditions where attackers operate with partial knowledge. Using data from 109 participants across diverse real-world datasets, our approach achieves 85% accuracy in re-identifying individuals in public datasets while maintaining a 14.2% overall misclassification rate at an optimal confidence threshold, with 15.6% of unknown individuals misclassified as known and 12.8% of known individuals misclassified as unknown. These results highlight the inadequacy of simple anonymization techniques in preventing re-identification, demonstrating that even limited adversarial knowledge enables effective identity linkage. Our findings underscore the urgent need for privacy-preserving strategies, such as differential privacy, access control, and encrypted computation, to mitigate re-identification risks while ensuring the utility of shared biosignal data in healthcare applications.