{"ID":6023478,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T09:03:54.148447248Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06037","arxiv_id":"2607.06037","title":"REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality","abstract":"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 \\emph{REAN} (\\emph{RE}construction-aware ECG \\emph{AN}onymizer), 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 ($\\approx$93.8$^\\circ$), so reducing privacy leakage leaves utility almost unchanged. On four public PhysioNet databases, REAN achieves the strongest privacy--utility balance among raw ECG signal baselines. It drives re-identification to chance (0.96$\\to$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.","short_abstract":"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 co...","url_abs":"https://arxiv.org/abs/2607.06037","url_pdf":"https://arxiv.org/pdf/2607.06037v1","authors":"[\"Taerin Ki\",\"Sunghwan Park\",\"Junyoung Park\",\"Jaewoo Lee\"]","published":"2026-07-07T09:12:53Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.LG\"]","methods":"[]","has_code":false}