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, 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 rec
The proliferation of AI in healthcare and biometric data collection is increasing the urgency for novel privacy-preserving techniques as regulations catch up with technological capabilities.
This development addresses a critical privacy-utility trade-off in biometric data sharing, potentially enabling broader and safer use of sensitive health information for AI model training and medical research.
The ability to anonymize raw ECG signals while preserving their utility for AI analysis marks a significant improvement over previous methods that compromised one for the other.
- · AI healthcare developers
- · Medical research institutions
- · Patients
- · Data privacy solution providers
- · Malicious actors
- · Companies with poor data privacy practices
Wider adoption of ECG data for AI diagnostics and personalized medicine due to enhanced privacy guarantees.
Increased trust in digital health platforms and greater patient willingness to share biometric data, accelerating AI development in healthcare.
The development of similar 'reconstruction-aware anonymization' techniques for other biometric and sensitive datasets, establishing a new standard for data privacy in AI applications.
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