Securing Healthcare Data with Advanced Asymmetric Lattice‎Galois Encryption Standard in A Decentralized Blockchain ‎Framework

  • Authors

    • K. Britto Alex Research Scholar, P.G. and Research Department of Computer Science, J.J. College of Arts and Science (Autonomous), Affiliated to ‎Bharathidasan University, Pudukkottai
    • K. Selvan Assistant Professor & Research Advisor, P.G. and Research Department of Computer Science, J.J. College of Arts and Science ‎‎(Autonomous), Pudukkottai‎, Affiliated to Bharathidasan University, Tiruchirappalli
    https://doi.org/10.14419/yg73n388

    Received date: July 25, 2025

    Accepted date: July 30, 2025

    Published date: November 1, 2025

  • Advanced Asymmetric Lattice Galois Encryption Standard (AALGES); Decentralized Blockchain; Decryption; Encryption; Preprocessing; ‎Python; Z-Score Normalization

  • Abstract

    Securing sensitive data is essential to protect against unauthorized access, breaches, and privacy violations. A decentralized blockchain ‎offers a robust solution by distributing data across multiple nodes, ensuring transparency, immutability, and enhanced protection, all without ‎relying on a central authority. Several clinical datasets are available, covering a wide range of healthcare-associated data. Encryption and ‎decryption, along with optimization, are techniques used to secure data by converting it into a form that is unintelligible without the precise ‎key or algorithm. These processes play a crucial role in data protection and privacy. These datasets are invaluable for numerous applications, ‎along with clinical studies, predictive analytics, system studies, and healthcare system optimization. The research presents an overall ‎approach to a safe data management case study, which uses Kaggle datasets. The approach begins with preprocessing using the Z-score ‎normalization method to ensure consistency in the dataset's dimensions. Use a data augmentation technique to expand the data range, thereby ‎increasing the recording duration. Thereafter, an encryption scheme is implemented to maximize the security of the dataset. The encoded ‎dataset is then placed in a decentralized blockchain, which allows for the effective utilization of its infrastructure for well-organized and ‎efficient storage methods. The decryption process is facilitated by an AALGES Exchange mechanism, which securely retrieves the original ‎data. Risk simulation occurs both before and after the data reaches the storage region, allowing for an evaluation of the system's resilience to ‎stress. This is done in the Python platform. To examine the system's performance, it simulates the analysis in detail, specifically focusing on ‎various aspects such as encryption and decryption speed, data storage capability, and minimum computational overhead‎.

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    Alex, K. B. ., & Selvan, K. (2025). Securing Healthcare Data with Advanced Asymmetric Lattice‎Galois Encryption Standard in A Decentralized Blockchain ‎Framework. International Journal of Basic and Applied Sciences, 14(SI-1), 641-648. https://doi.org/10.14419/yg73n388