Blockchain-based secure provenance and auditability mechanism for medical IoT devices in healthcare ecosystems

Abstract

The increasing approval of Internet of Medical Things (IoMT) in healthcare produces large volumes of sensitive patient data, raising critical challenges in security, privacy, and integrity due to weaknesses in centralized systems. Such systems are prone to tampering, unauthorized access, and a lack of transparency, generating risks for patient safety and regulatory compliance. This study is motivated by the need for a secure, transparent, and tamper-resistant data management framework that ensures trustworthy medical data handling. The proposed method leverages blockchain technology to qualify decentralized provenance tracking and immutable auditability of IoMT data across healthcare ecosystems. The main objective is to design a secure framework incorporating adaptive noise filtering, blockchain-based provenance logging, decentralized access control, and forensic verification for reliable healthcare data management. The key contribution lies in emerging an integrated AINF-SHPP-DAVE-FTIM pipeline that increases data integrity, access security, and forensic readiness while improving scalability and performance for real-time IoMT healthcare applications and regulatory compliance. The proposed methodology incorporates AINF, SHPP, DAVE, and FTIM for secure IoMT healthcare data management using NS-3 hospital emulation with 100 nodes and MIMIC-III-based processing. AINF achieves 92.35 ± 2.1 % noise reduction with 28.4 ± 2.1 dB SNR, SHPP delivers 15.8 ± 2.1 ms hashing latency and 110 ± 8 t x / s throughput, DAVE certifies 98.2 ± 0.5 % access accuracy with 0.35 ± 0.1 % false positives, and FTIM delivers 99.825 ± 0.15 % forensic verification with 127.5 ± 8.2 ms response time. Results confirm strong scalability across 50–500-bed hospital settings with stable performance under load variations. Future scope includes enlargement to 1000 + IoMT nodes, integration of post-quantum cryptography, edge-AI optimization, and advanced federated learning for improved anomaly detection, privacy, and real-time clinical deployment in large-scale healthcare ecosystems.