An adaptive learning model for secure data sharing in decentralized environments using blockchain technology

Blockchain technology has rapidly emerged as a vital skillset, reshaping digital infrastructure with its decentralized, transparent, ‎and secure characteristics. These core features have driven its integration across various industrial applications, establishing it as ‎a foundation for modern computing paradigms such as cloud and edge computing. Recognizing this potential, the present study ‎introduces a novel and disruptive approach utilizing an adaptive learning model to enhance security within data-sharing ‎ecosystems through decentralized access control mechanisms. The proposed framework was implemented using Python and ‎rigorously evaluated through experimentation. A comprehensive performance analysis compared the proposed ‎Adaptive Learning Model (ALM) against conventional cryptographic techniques, specifically RSA and AES algorithms. ‎Multiple performance metrics were analysed, and the outcomes demonstrated that the proposed method significantly improves security, scalability, and processing time‎.