Enhancing Data Privacy in IoMT Healthcare Systems Using Digital Twins and Blockchain Technologies

Abstract

The Internet of Medical Things (IoMT) enables continuous health monitoring by integrating wearable sensors, medical devices, and ambient systems. However, pervasive data collection across edge, fog, and cloud layers raises significant privacy risks, including disclosure of Personally Identifiable Information (PII), re-identification, and unauthorized secondary use. This paper proposes a privacy-by-design architecture that couple’s patient-specific Digital Twins (DTs) with a permissioned blockchain to enforce consent, provenance, and tamper-evident auditing. The architecture separates data and control planes: clinical data remain off-chain in standards-compliant repositories (FHIR/HL7/DICOM), while minimal hashes, access policies, and consent receipts are committed on-chain using smart contracts. We incorporate differential privacy for analytics, homomorphic encryption for computation over encrypted data, zero-knowledge proofs for access attestation, and federated learning for device/edge model training without raw data sharing. A threat model aligned with STRIDE guides our choice of controls, including secure key management and runtime verification of smart contracts. We outline an evaluation methodology using privacy risk, utility, latency, and throughput metrics, and present a case study for remote cardiac monitoring. Results from a reference implementation indicate that the proposed approach can reduce privacy risk and strengthen accountability with acceptable overheads for real-time clinical use. We conclude with deployment guidance and open challenges around governance, interoperability, and formal guarantees.