Secure Cloud Data Distribution using Blockchain and Explainable Deep Learning for Enhanced Privacy

The distribution of sensitive information in the cloud presents significant privacy and security challenges, especially for industries such as defense and healthcare, where safeguarding data is critical for collaboration and decision-making.The growing reliance on cloud servers for data storage by enterprises and their clients exacerbates risks to user privacy and data security.This study proposes a novel, secure cloud data distribution system designed to offer dynamic user access while addressing confidentiality concerns.The methodology leverages blockchain technology to enable decentralized and distributed data storage, ensuring tamper-proof transactions and enhanced security through tokenized value systems.A feature attribution-explainable deep learning model (FA-XDLM) is integrated into the framework to enhance transparency and security.This model highlights the significance of individual data features in influencing predictions, offering interpretability for data access and processing.The encryption mechanism within the FA-XDLM framework further protects sensitive information by securing data during storage and transmission.Blockchain technology is employed to confidently transmit data to cloud servers, implementing advanced storage methods to uphold data privacy.Experimental evaluations reveal the proposed system's high effectiveness, achieving an accuracy rate of 99.12%.This approach not only addresses data security challenges but also fosters trust in cloud environments by providing reliable access, confidentiality, and privacy.The combination of blockchain technology with explainable AI ensures an innovative solution for secure cloud data distribution, meeting the growing demands of sensitive industries.