Confidential Computing in Public Cloud: Architectures for Privacy-Preserving Workloads

Confidential computing is rapidly emerging as a critical technology for enabling privacy-preserving workloads in public cloud environments. By leveraging hardware-based trusted execution environments (TEEs), sensitive data can remain encrypted in memory during processing, significantly mitigating risks associated with cloud provider access, malicious insiders, and advanced persistent threats. This paper explores architectural patterns and design considerations for deploying privacy-sensitive applications utilizing confidential computing capabilities offered by major public cloud providers. We analyze various approaches for integrating TEEs into cloud-native frameworks, addressing key challenges related to data ingress/egress, attestation, key management, and the orchestration of confidential workloads. Furthermore, we discuss the trade-offs associated with performance, compatibility, and application lifecycle management. The proposed architectural blueprints aim to provide a practical foundation for technical architects designing systems that demand strong data confidentiality guarantees while harnessing the scalability and flexibility of the public cloud.