GAHBT: Genetic Based Hashing Algorithm for Managing and Validating Health Data Integrity in Blockchain Technology

Abstract

This research work proposes a method for managing, securing, and validating the health data distribution records using a genetic-based hashing algorithm in a decentralized environment. The reason behind choosing blockchain is to secure the transaction of health data and protect the data from manipulated fraudulent movement and corruption by the contributor of the chain or any individual. The path of this technology approach provides an efficient surveillance measure such as transparency of record, fraud immunity, and protection to tempering and sustaining the order of data. In medical research, this paper provides a genetic-based hashing algorithm for data security which has lower computational complexity, low space coverage, higher security and integrity, and a high avalanche effect. The simulation will show data records' validity, immunity, and integrity. The technique modified in this secure decentralized network is a cryptographic hashing algorithm for 512 bits. In this study genetic algorithm is used to generate a key that needs to be used in the encryption and decryption of medical data. A genetic algorithm is a metaheuristic approach that is inspired by the laws of genetics and is generally used to generate high-quality solutions for complex problems. Applications of genetic algorithms encourage the significance in many medical fields such as radiology, oncology, cardiology, endocrinology, surgery, oncology, and radiotherapy in health care management.

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

“…To ensure alignment, a user may, for instance, publish a hashed version of their data along with the key, allowing recipients to compare their computed hash value to the published value. The study [173] suggests a reliable approach that makes use of a cryptographic hash function and the conventional cryptographic method to create a new hash. The system presented here uses a genetic algorithm (GA) approach for optimisation and is backed by a hashing algorithm to enhance overall data surveillance.…”