Confidential Computing in Public Clouds : Confidential Data Translations in hardware-based TEEs: Intel SGX with Occlum support

Abstract: As enterprises migrate their data to cloud infrastructure, they increasingly need a flexible, scalable, and secure marketplace for collaborative data creation, analysis, and exchange among enterprises. Security is a prominent research challenge in this context, with a specific question on how two mutually distrusting data owners can share their data. Confidential Computing helps address this question by allowing to perform data computation inside hardware-based Trusted Execution Environments (TEEs) which we refer to as enclaves, a secured memory that is allocated by CPU. Examples of hardware-based TEEs are Advanced Micro Devices (AMD)-Secure Encrypted Virtualization (SEV), Intel Software Guard Extensions (SGX) and Intel Trust Domain Extensions (TDX). Intel SGX is considered as the most popular hardware-based TEEs since it is widely available in processors targeting desktop and server platforms. Intel SGX can be programmed using Software Development Kit (SDK) as development framework and Library Operating Systems (Library OSes) as runtimes. However, communication with software in the enclave such as the Library OS through system calls may result in performance overhead. In this project, we design confidential data transactions among multiple users, using Intel SGX as TEE hardware and Occlum as Library OS. We implement the design by allowing two clients as data owners share their data to a server that owns Intel SGX capable platform. On the server side, we run machine learning model inference with inputs from both clients inside an enclave. In this case, we aim to evaluate Occlum as a memory-safe Library Operating System (OS) that enables secure and efficient multitasking on Intel SGX by measuring two evaluation aspects such as performance overhead and security benefits. To evaluate the measurement results, we compare Occlum with other runtimes: baseline Linux and Graphene-SGX. The evaluation results show that our design with Occlum outperforms Graphene-SGX by 4x in terms of performance. To evaluate the security aspects, we propose 11 threat scenarios potentially launched by both internal and external attackers toward the design in SGX platform. The results show that Occlum security features succeed to mitigate 10 threat scenarios out of 11 scenarios overall.