Secure Multi-Cloud Data Analysis: Privacy-Preserving Deep Neural Networks for Confidential Computing

Abstract

Moving large data sets into multi-cloud systems generates significant security and privacy challenges while processing sensitive information. The access traditional framework deep learning models need to raw information creates the potential for security threats through data exposure and permission violations. The paper studies how privacy-preserving deep neural networks (PP-DNNs) operate as tools for confidential computing across multi-cloud deployments. The combination of TTwenty homomorphic encryption with secure multi-party computation (SMPC) and differential privacy and trusted execution environments (TEEs) makes it possible to execute complex neural network models while keeping all data fully confidential. Our framework uses secure deep learning capabilities across multiple cloud providers while securing data by keeping it secret, protecting privacy, and fulfilling regulatory criteria. Our research examines our methodology's performance while maintaining model accuracy, secure data privacy protocols, and execution speed. Science shows that PP-DNNs answer the requirements of healthcare, finance, and cybersecurity by providing secure private data analytics solutions that scale across different operating environments.