提出REAN方法，让心电图在保护隐私的同时保持诊断可用性。
REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality
- 用1D U-Net结合冻结分类器损失，实现隐私与实用性的近正交优化。
- 在四个数据库上实现0.96→0.00的重识别率下降，心律失常识别性能几乎不变。
- 适用于医疗数据共享场景，尤其适合关注隐私与诊断质量平衡的研究者。
共享的心电图（ECG）本身是生物特征指纹，可能重新识别患者并泄露个人信息。现有匿名化方法在保护隐私与保持信号可用性之间存在权衡：保护隐私会损害可用性，反之亦然。本文提出重建感知的原始心电信号匿名化方法REAN，通过训练一维U-Net，利用冻结的隐私与实用性分类器的损失，在降低隐私泄露的同时保持信号价值。隐私与实用性的梯度接近正交（约93.8°），因此减少隐私泄露对实用性影响极小。在四个公开的PhysioNet数据库上，REAN在原始心电图基线中实现了最优的隐私-实用性平衡：重识别率从0.96降至0.00，心律失常宏平均受试者工作特征曲线下面积（macro-AUROC）维持在清洁数据水平（清洁0.9982 vs. REAN 0.9991），且对未见过的隐私分类器架构仍具备强防护能力。
原文摘要 · Abstract (English)
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.
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