A Hybrid Post-Quantum and Blockchain-Based Model for Tamper-Proof Healthcare Data Systems

Abstract

Quantum-Resistant Blockchain for Secure Health Data is designed to tackle increasing security challenges in healthcare systems caused by outdated encryption methods and centralized data storage. In India, incidents of healthcare data breaches have risen significantly, including the 2021 breach of a government health database that exposed over 100 million records. With the rapid growth of digital health records, telemedicine, and e-governance, there is a critical need for quantum-resistant cryptographic solutions to protect patient information. As initiatives like the Ayushman Bharat Digital Mission (ABDM) advance digital healthcare ecosystems, combining blockchain with post-quantum security becomes essential for ensuring decentralized, tamper-proof, and future-ready data management. The primary objective of this work is to develop a secure and decentralized healthcare data system by integrating blockchain technology with quantum-resistant cryptographic mechanisms, ensuring data confidentiality, integrity, and support for machine learning-based diagnostics. Traditional healthcare data management systems rely on physical records, spreadsheets, or basic databases where patient information is manually entered and shared across departments. Such approaches often result in inefficiencies, delays, and weak access control. These systems are prone to human errors, document degradation, data redundancy, unauthorized access, limited historical tracking, absence of real-time analysis, and lack of transparency or auditability. This research is motivated by the shortcomings of manual and partially digital systems that cannot provide scalable and robust security. By leveraging blockchain and post-quantum cryptography, the proposed solution mitigates risks associated with future quantum attacks while ensuring secure communication, immutable records, and privacy-preserving data analysis within a tamper-resistant framework. Additionally, the system integrates machine learning models to analyze decrypted health data for diagnostic support and risk prediction. Advanced postquantum cryptographic algorithms such as FALCON for digital signatures and KYBER for key encapsulation are employed to secure the system. Blockchain technology records all activities related to file access, uploads, and downloads, ensuring traceability, transparency, and resistance to data tampering