Integrating Hybrid Encryption for Privacy in Blockchain Based Healthcare Contracts with Improved Convolutional Coded Merkle Trees
S. M., Rekha B Venkatapur · Journal of Machine and Computing · 2025
Privacy is a challenging task in a permissioned Blockchain because all data are available to all the participants of the network. Hence permitting, sharing, and storing health data in a reasonably secured manner with full privacy is essential, besides giving the owner full control of data. Deep learning methods have been explored as one of the effective and efficient ways to discover smart contract vulnerabilities because of their high detection accuracy and rapid detection speed. This research argues that the traditional database systems, conventional security mechanisms, and legacy access control policies are insufficient in maintaining the interoperability, privacy, and security of healthcare systems. This research proposes a novel approach to enhance digital signatures by integrating hybrid encryption techniques for privacy preservation in blockchain-based healthcare contracts. The key innovation lies in the introduction of Improved Convolutional Coded Merkle Trees (ICCMT) to improve data integrity and security. Our study addresses the pressing need for robust security measures in healthcare data management within blockchain environments. By combining hybrid encryption methods with convolutional coded Merkle trees, we aim to strengthen the confidentiality and integrity of patient information. This approach not only enhances privacy but also ensures the reliability of healthcare contracts stored on the blockchain. The proposed framework represents a significant step towards building secure and privacy-preserving solutions for healthcare systems leveraging blockchain technology.