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Trusted execution environments (TEEs) have been proposed to protect GPU computation for machine learning applications operating on sensitive data. However, existing GPU TEE solutions either require CPU and/or GPU hardware modification to realize TEEs for GPUs, which prevents current systems from adopting them, or rely on untrusted system software such as GPU device drivers. In this paper, we propose using CPU secure enclaves, e.g., Intel SGX, to build GPU TEEs without modifications to existing hardware. To tackle the fundamental limitations of these enclaves, such as no support for I/O operations, we design and develop GEVisor, a formally verified security reference monitor software to enable a trusted I/O path between enclaves and GPU without trusting the GPU device driver. GEVisor operates in the Virtual Machine Extension (VMX) root mode, monitors the host system software to prevent unauthorized access to the GPU code and data outside the enclave, and isolates the enclave GPU context from other contexts during GPU computation. We implement and evaluate GEVisor on a commodity machine with an Intel SGX CPU and an NVIDIA Pascal GPU. Our experimental results show that our approach maintains an average overhead of 13.1% for deep learning and 18% for GPU benchmarks compared to native GPU computation while providing GPU TEEs for existing CPU and GPU hardware.
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This content will become publicly available on January 3, 2026
Confidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey Study
ABSTRACT BackgroundConfidential computing has gained prominence due to the escalating volume of data‐driven applications (e.g., machine learning and big data) and the acute desire for secure processing of sensitive data, particularly across distributed environments, such as the edge‐to‐cloud continuum. ObjectiveProvided that the works accomplished in this emerging area are scattered across various research fields, this paper aims at surveying the fundamental concepts and cutting‐edge software and hardware solutions developed for confidential computing using trusted execution environments, homomorphic encryption, and secure enclaves. MethodsWe underscore the significance of building trust at both the hardware and software levels and delve into their applications, particularly for regular and advanced machine learning (ML) (e.g., large language models (LLMs), computer vision) applications. ResultsWhile substantial progress has been made, there are some barely‐explored areas that need extra attention from the researchers and practitioners in the community to improve confidentiality aspects, develop more robust attestation mechanisms, and address vulnerabilities of the existing trusted execution environments. ConclusionProviding a comprehensive taxonomy of the confidential computing landscape, this survey enables researchers to advance this field to ultimately ensure the secure processing of users' sensitive data across a multitude of applications and computing tiers.
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- Award ID(s):
- 2417064
- PAR ID:
- 10576901
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Software: Practice and Experience
- ISSN:
- 0038-0644
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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