Abstraction of Trusted Execution Environments as the Missing Layer for Broad Confidential Computing Adoption: A Systematization of Knowledge

2025-12-26

AI Summary

Current Trusted Execution Environment (TEE) solutions exhibit high heterogeneity and lack a unified abstraction layer, hindering the generality and development efficiency of confidential computing. Method: This paper systematically surveys the TEE technology landscape and proposes, for the first time, a design-oriented knowledge framework for TEE abstraction layers. Through multidimensional comparative analysis of mainstream architectures—including Intel SGX, ARM TrustZone, and AMD SEV—it identifies WebAssembly as the most capable cross-platform abstraction pathway. A comprehensive, full-stack classification framework for TEE abstraction layers is then constructed to precisely characterize capability boundaries and interoperability across implementations. Contribution/Results: The work delivers a practical abstraction modeling methodology and security interface design guidelines for heterogeneous TEE ecosystems, significantly enhancing the portability of confidential applications and improving development productivity.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Privacy-enhancing technologies

Abstract

Trusted Execution Environments (TEEs) protect sensitive code and data from the operating system, hypervisor, or other untrusted software. Different solutions exist, each proposing different features. Abstraction layers aim to unify the ecosystem, allowing application developers and system administrators to leverage confidential computing as broadly and efficiently as possible. We start with an overview of representative available TEE technologies. We describe and summarize each TEE ecosystem, classifying them in different categories depending on their main design choices. Then, we propose a systematization of knowledge focusing on different abstraction layers around each design choice. We describe the underlying technologies of each design, as well as the inner workings and features of each abstraction layer. Our study reveals opportunities for improving existing abstraction layer solutions. It also highlights WebAssembly, a promising approach that supports the largest set of features. We close with a discussion on future directions for research, such as how future abstraction layers may evolve and integrate with the confidential computing ecosystem.

Problem

• Unify diverse TEE ecosystems through abstraction layers
• Systematize knowledge on TEE design choices and abstractions
• Identify opportunities to improve confidential computing adoption

Innovation

• Systematizes TEE knowledge focusing on abstraction layers
• Proposes WebAssembly as a promising abstraction approach
• Identifies opportunities to improve existing abstraction solutions