A Blockchain-Enabled Adaptive Learning Model for Secure and Scalable Data Sharing

The blockchain skillset is one of the most emerging skill sets that brings the world into the hands ‎of the self. The number of industrial applications depends on this new technology just because of its ‎decentralized, transparent, and secure nature. This enables a new way for the next generation ‎of computing environments like cloud computing and edge computing. By keeping this in mind, ‎this work develops a new disruptive method using adaptive learning model to address the ‎security issues in a data sharing environment with decentralized access control. The developed ‎framework has been executed and tested utilizing Python, and the results have been ‎presented. A performance study comparison between the existing RSA algorithm, AES ‎algorithm, and the proposed algorithm (ALM) has been done, and the various parameters ‎taken for the study and their values are presented in this paper. Results obtained show that the ‎algorithm presented is proven to be efficient in terms of security, scalability and time‎.