# REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality

arXiv: https://arxiv.org/abs/2607.06037 · HTML: https://mlsanity.com/paper/2607.06037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets](https://mlsanity.com/paper/2408.10228/) (2024)
- [Linkage Attacks Expose Identity Risks in Public ECG Data Sharing](https://mlsanity.com/paper/2508.15850/) (2025)
- [TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis](https://mlsanity.com/paper/2503.13495/) (2025)
- [Privacy-Preserving ECG Data Analysis with Differential Privacy: A Literature Review and A Case Study](https://mlsanity.com/paper/2406.13880/) (2024)
- [SHDB-AF: a Japanese Holter ECG database of atrial fibrillation](https://mlsanity.com/paper/2406.16974/) (2024)

## Abstract (arXiv)

A shared electrocardiogram (ECG) is itself a biometric fingerprint that can re-identify a patient and reveal personal information. Recent ECG anonymizers transform the signal before sharing to reduce privacy leakage. However, existing methods still face a privacy--utility trade-off, in which preserving privacy often compromises utility while preserving utility reveals personal information. We propose REAN (REconstruction-aware ECG ANonymizer), a raw ECG signal anonymizer, to address this privacy--utility trade-off. REAN reconstructs the signal using a 1-D U-Net trained with losses from frozen privacy and utility classifiers to reduce privacy leakage while preserving utility. The privacy and utility gradients are near-orthogonal (≈93.8^circ), so reducing privacy leakage leaves utility almost unchanged. On four public PhysioNet databases, REAN achieves the strongest privacy--utility balance among raw ECG signal baselines. It drives re-identification to chance (0.96→0.00), keeps arrhythmia macro-AUROC at the clean level (Clean 0.9982 vs. REAN 0.9991), and maintains re-identification protection under unseen privacy-classifier architectures.