Embedding and retrieving private metadata in electrocardiograms.
Due to the recent explosion of 'identity theft' cases, the safeguarding of private data has been the focus of many scientific efforts. Medical data contain a number of sensitive attributes, whose access the rightful owner would ideally like to disclose only to authorized personnel. One way of providing limited access to sensitive data is through means of encryption. In this work we follow a different path, by proposing the fusion of the sensitive metadata within the medical data. Our work is focused on medical time-series signals and in particular on Electrocardiograms (ECG). We present techniques that allow the embedding and retrieval of sensitive numerical data, such as the patient's social security number or birth date, within the medical signal. The proposed technique not only allows the effective hiding of the sensitive metadata within the signal itself, but it additionally provides a way of authenticating the data ownership or providing assurances about the origin of the data. Our methodology builds upon watermarking notions, and presents the following desirable characteristics: (a) it does not distort important ECG characteristics, which are essential for proper medical diagnosis, (b) it allows not only the embedding but also the efficient retrieval of the embedded data, (c) it provides resilience and fault tolerance by employing multistage watermarks (both robust and fragile). Our experiments on real ECG data indicate the viability of the proposed scheme.
Kozat
Suleyman S
SS
Koç University, Istanbul, Turkey.
Vlachos
Michail
M
Lucchese
Claudio
C
Van Herle
Helga
H
Yu
Philip S
PS
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis; D016494:Computer Security / Q000295:instrumentation; D003219:Confidentiality; D004562:Electrocardiography / Q000295:instrumentation*; D001330:Electronic Data Processing / Q000295:instrumentation* / Q000379:methods*; D005568:Forms and Records Control / Q000379:methods; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D016347:Medical Records Systems, Computerized / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012943:Social Security; D012985:Software Design
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