Integration of Confidential Computing in Multi-Cloud Environments for Enhancing Data Privacy and Regulatory Compliance through Encrypted Processing and Isolated Execution

Abstract

The proliferation of multi-cloud environments has amplified data privacy risks and regulatory compliance challenges, necessitating advanced security paradigms. This study investigates the integration of confidential computing leveraging trusted execution environments (TEEs) to enable encrypted processing and isolated execution for mitigating these challenges. Employing a mixed-methods approach, including simulation-based analysis of hypothetical yet realistic datasets representative of healthcare and financial domains, the research evaluates performance overheads, estimated data breach cost reductions, and regulatory compliance efficacy within multi-cloud configurations. The findings indicate that, under controlled experimental conditions, confidential computing mechanisms can reduce projected data breach costs by approximately 15–20% and lower simulated regulatory violation rates under frameworks such as GDPR and HIPAA by up to 25%. The methodological framework incorporates established confidential computing technologies, including Intel SGX and AWS Nitro Enclaves, with performance and compliance indicators analyzed using statistical tools such as Python-based Pandas and SciPy libraries. The conclusions highlight the potential of confidential computing, as understood up to 2022, to enhance secure and compliant multi-cloud operations by strengthening data protection during processing. The study contributes theoretically by examining interoperability considerations among TEEs and practically by offering design-oriented insights for secure multi-cloud architectures. Overall, the work addresses existing limitations in privacy-preserving computation across heterogeneous cloud infrastructures within the contemporary technological and regulatory landscape.