Privacy-Preserving Patient Identity Management Framework for Secure Healthcare Access
This framework provides a solution for healthcare providers to manage patient identities securely, addressing privacy concerns for patients while ensuring accurate record-keeping, which is an incremental improvement in healthcare data management.
This paper addresses the challenge of patient identity management in healthcare, aiming to balance operational reliability with strong privacy. It proposes a framework that utilizes a rooted trust anchor, anonymous pseudonyms, and conditional traceability to protect patient privacy while maintaining accurate longitudinal health records. The framework's identity workflows were shown to be operationally feasible within typical clinical latency bounds through simulation.
Effective healthcare delivery depends on accurate longitudinal health records and addressing patients' concerns regarding the privacy of their information. While patient authentication is essential, reusing patient identifiers exposes individuals to linkability (associating multiple visits) and traceability (tying visits to real-world identities) risks. This paper presents a privacy-preserving, patient-centric identity management framework specifically tailored to the operational and regulatory requirements of healthcare. The framework balances operational reliability with strong privacy protections through a rooted trust anchor, anonymous pseudonyms, and a conditional traceability mechanism. It is formally specified, and its security and privacy properties are evaluated through MSRA-based architectural analysis and complementary formal verification. Simulation-based evaluation demonstrates that the framework's identity workflows are operationally feasible within the latency bounds typical of clinical environments.