Secure Data Sharing with Confidential Computing Enclaves
DOI:
https://doi.org/10.15662/IJRAI.2024.0705001Keywords:
Confidential Computing, Trusted Execution Environment (TEE), Secure Data Sharing, Intel SGX, AMD SEV, ARM TrustZone, Remote Attestation, Privacy-Preserving Computation, Side-Channel Attacks, Data ConfidentialityAbstract
Secure data sharing is a critical requirement in today’s data-driven world, especially when dealing with sensitive information across organizational boundaries. Traditional data protection techniques often fall short in addressing threats from malicious insiders, compromised cloud environments, or unauthorized access during data processing. Confidential Computing Enclaves (CCEs) have emerged as a groundbreaking technology that leverages hardware-based Trusted Execution Environments (TEEs) to protect data in use by isolating computations from other system components. This paper explores the role of confidential computing enclaves in enabling secure data sharing across untrusted environments while preserving privacy and confidentiality. We provide an overview of enclave technologies such as Intel SGX, AMD SEV, and ARM TrustZone, highlighting their architectures and security guarantees. The study surveys cryptographic techniques integrated with enclaves, including remote attestation, secure key management, and data sealing, which collectively ensure end-to-end security in collaborative data sharing scenarios. We further analyze recent frameworks and platforms that employ confidential computing enclaves for secure multiparty computations, privacy-preserving analytics, and data collaboration. By evaluating their performance, scalability, and security aspects, the paper identifies key challenges such as enclave memory limitations, side-channel attacks, and trust establishment. The research methodology includes experimental evaluation of data sharing workflows within confidential computing environments, measuring overheads and security trade-offs. Results demonstrate that confidential computing enclaves significantly reduce the attack surface while maintaining acceptable performance for practical use cases. Finally, the paper discusses future directions including improved enclave architectures, enhanced side-channel mitigations, and integration with blockchain and secure hardware accelerators to bolster trust and transparency in data sharing. This work aims to guide researchers and practitioners toward robust, privacy-preserving data sharing solutions powered by confidential computing.
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