Confidential Computing in the Cloud: Cryptographic Isolation for Trusted Execution

Abstract

Confidential computing has emerged as a transformative paradigm in cloud security, enabling sensitive workloads to be processed in encrypted form even while in use. Unlike traditional cloud security models that protect data at rest and in transit, confidential computing introduces hardware-backed trusted execution environments (TEEs) that isolate computation from the host operating system, hypervisor, and cloud provider administrators. This paper presents a comprehensive study of confidential computing architectures, cryptographic isolation mechanisms, and their implications for multi-tenant cloud platforms. We analyze modern TEE implementations, including Intel SGX and TDX, AMD SEV-SNP, and ARM TrustZone, and examine how cryptographic primitives, remote attestation, and secure memory encryption jointly establish trustworthy execution domains. A prototype evaluation is conducted on representative confidential virtual machine and enclave-based deployments to quantify performance overhead, scalability, and resistance to privileged adversaries. Experimental results demonstrate that while cryptographic isolation introduces moderate latency and memory overhead, careful system design and workload-aware optimization substantially mitigate these costs, making confidential computing practical for data analytics, machine learning inference, and regulated workloads. Finally, we discuss open research challenges, including side-channel resistance, programmability, orchestration at scale, and the integration of confidential computing into emerging confidential AI pipelines.