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New REAN technique balances ECG privacy and utility

Researchers have developed REAN, a novel anonymization technique for electrocardiogram (ECG) data that addresses the long-standing privacy-utility trade-off. REAN utilizes a 1-D U-Net architecture trained with privacy and utility classifiers to reconstruct ECG signals. This method achieves near-orthogonal gradients between privacy and utility, allowing for significant privacy enhancement with minimal impact on data utility. Tested on public PhysioNet databases, REAN effectively reduces re-identification risk to chance levels while maintaining high accuracy for arrhythmia detection. AI

IMPACT This research could lead to more secure sharing of sensitive health data, enabling broader research while protecting patient privacy.

RANK_REASON The cluster contains a research paper detailing a new anonymization technique for ECG data.

Read on arXiv cs.LG →

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New REAN technique balances ECG privacy and utility

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Taerin Ki, Sunghwan Park, Junyoung Park, Jaewoo Lee ·

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

    arXiv:2607.06037v1 Announce Type: cross 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, exis…

  2. arXiv cs.LG TIER_1 English(EN) · Jaewoo Lee ·

    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-o…