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

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.