Blockchain integrated AI framework for secure data sharing

Department of Computer Engineering, Marathwada Mitra Mandal’s Institute of Technology, Pune, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 26 June 2026

Abstract

As a result of the rapid rise of digital data, privacy, security and trust issues will arise in sharing data between organizations across multiple distributed environments. Areas like healthcare, finance, the Internet of Things (IoT) and smart companies need solid solutions to allow for secure and occurring exchange of data. Traditional centralized systems present a greater risk to a large number of companies due to the potential for one point of failure, restricted ability to audit and view transactions on the system, and little to no transparency into the transaction process on the system. Because of these limitations, centralized systems will not be able to support the needs of these vast-scale collaborative ecosystems. This paper provides a proposed Blockchain-Integrated AI Framework for Secure Data Sharing, which provides a variety of capabilities necessary for securely exchanging data between organizations that includes decentralized trust, encrypted off-chain data storage and use of privacy-protecting artificial intelligence analytics. The framework includes the use of Ethereum Smart Contracts to provide an immutable access control and permission management process and create comprehensive audit trails. An Inter-planetary File System (IPFS) is used to provide decentralized, encrypted access to sensitive data stored in an environment that provides for confidentiality and availability. The use of a Python-based AI Microservice allows access to the shared data in an analytical process by eliminating the need for raw data to be exposed. The proposed implementation of this framework is based on a hybrid architecture consisting of a Node.js back-end and a React.js front-end, which allow for real-time monitoring and transparent management of the access control function, which serves to enhance the integrity, confidentiality and secure collaboration of the shared data, and enables the development of scalable analytics across distributed ecosystems, and therefore provides.