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In IEEE journal of biomedical and health informatics

Healthcare data for primary use (diagnosis) may be encrypted for confidentiality purposes; however, secondary uses such as feeding machine learning algorithms requires open access. Full anonymity has no traceable identifiers to report diagnosis results. Moreover, implicit and explicit consent routes are of practical importance under recent data protection regulations (GDPR), translating directly into break-the-glass requirements. Pseudonymisation is an acceptable compromise when dealing with such orthogonal requirements and is an advisable measure to protect data. Our work presents a pseudonymisation protocol that is compliant with implicit and explicit consent routes. The protocol is constructed on a (t,n)-threshold secret sharing scheme and public key cryptography. The pseudonym is safely derived from a fragment of public information without requiring any data-subject's secret. The method is proven secure under reasonable cryptographic assumptions and scalable from the experimental results.

Pedrosa Micael, Zuquete Andre, Costa Carlos

2020-Oct-02