Algorand-proof-of-stake blockchain based-digitalized healthcare data security using protected smart contract and behavioural analyzation.

Authors

D Bala Gayathri, D Sangeetha

Published in

Computational biology and chemistry. Volume 126. Issue Pt 1. Pages 109434. Sep 26, 2026. Epub Sep 26, 2026.

Abstract

Digitized Healthcare Data (DHD) enables efficient patient care through secure and accessible electronic medical records. However, vulnerabilities in blockchain-based Smart Contracts (SCs) compromise the integrity and confidentiality of Medical Data (MD), making healthcare systems susceptible to unauthorized access and cyberattacks. Hence, this study proposes an integrated blockchain-centric model to enhance DHD security by combining privacy preservation, encryption, attack detection, anomaly prediction, and secure access control. Initially, Brennan-Surprisal-based K-anonymity (BS-K-anonymity) is used to preserve patient privacy, followed by Cathedral q-Cipher-based Advanced Rényi Encryption Standard (CqC-ARES) for secure data encryption. The encrypted data are further analyzed using a Tsallis-Adaptive Pooling-based Convolutional Neural Network (TAP-CNN) to detect Structured Query Language (SQL) injection attacks before being stored in the Algorand-Proof-of-Stake Blockchain (A-BC). Smart contracts are protected using Ardoq-based Role-Based Access Control (A-RBAC), while Chi-Shannon Density-Based Spatial Squared Clustering of Applications with Noise (CSDB2SCAN) performs anomaly prediction through user behavior analysis. Finally, secure blockchain transactions are achieved using Sharding Federated Proof of Decay Stake (SFPoDS). As per experimental outcomes, the proposed model attains 99.17% SQL attack detection accuracy, 99.24% anomaly prediction accuracy, 97.83% access control enforcement rate, and 98% security incident detection rate, outperforming existing approaches. Therefore, the proposed framework offers a secure, reliable, and effective solution for blockchain-enabled healthcare data management.

PMID:
42828973
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.

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