Abstract / truncated to 115 words (read the full abstract)

To preserve the privacy of patients and service providers in biomedical signal processing applications, particular attention has been given to the use of secure multiparty computation techniques. This thesis focuses on the development of a privacy preserving automatic diagnosis system whereby a remote server classifies a biomedical signal provided by the patient without getting any information about the signal itself and the final result of the classification. Specifically, we present and compare two methods for the secure classification of electrocardiogram (ECG) signals: the former based on linear branching programs and the latter relying on neural networks. Moreover a protocol that performs a preliminary evaluation of the signal quality is proposed. The thesis deals with all ... toggle 9 keywords

secure computing – multi-party computation – homomorphic encryption – garbled circuit – ECG – electrocardiograms – biomedical analysis – ecg classification – quality evaluation

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