SoK: Hardware-supported Trusted Execution Environments
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Abstract (arXiv)
The growing complexity of modern computing platforms and the need for strong isolation protections among their software components has led to the increased adoption of Trusted Execution Environments (TEEs). While several commercial and academic TEE architectures have emerged in recent times, they remain hard to compare and contrast. More generally, existing TEEs have not been subject to a holistic systematization to understand the available design alternatives for various aspects of TEE design and their corresponding pros-and-cons. Therefore, in this work, we analyze the design of existing TEEs and systematize the mechanisms that TEEs implement to achieve their security goals, namely, verifiable launch, run-time isolation, trusted IO, and secure storage. More specifically, we analyze the typical architectural building blocks underlying TEE solutions, design alternatives for each of these components and the trade-offs that they entail. We focus on hardware-assisted TEEs and cover a wide range of TEE proposals from academia and the industry. Our analysis shows that although TEEs are diverse in terms of their goals, usage models, and instruction set architectures, they all share many common building blocks in terms of their design.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2022.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoningcited by 1
- 2025Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Thingscited by 1
- 2026Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmapcited by 0
- 2026HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systemscited by 0
- 2026HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems1 Oct 2026
- 2026Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap13 Mar 2026
- 2025Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things26 May 2025
- 2024Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning10 May 2024