Lite

Yudha, Ardhi Wiratama Baskara; Meyer, Jake; Yuan, Shougang; Zhou, Huiyang; Solihin, Yan

doi:10.1145/3524059.3532361

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Such modification severely reduce the compatibility and may not adapt to endpoints. LITE [82] proposes a co-design framework between CPU TEE and its GPU TEE though it is not adapted to endpoint GPUs. Existing Arm TEEs (e.g., SANCTUARY [72], TrustICE [83], Inktag [84], Trustshadow [85] and vTZ [73]) leverage the non-secure [73] and secure [40] Stage-2 translation to achieve access control, or protect the untrusted applications with traditional TrustZone techniques [84], [85].…”

Section: Related Workmentioning

confidence: 99%

Building a Lightweight Trusted Execution Environment for Arm GPUs

Wang,

Deng,

Ning

et al. 2024

IEEE Trans. Dependable and Secure Comput.

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A wide range of Arm endpoints leverage integrated and discrete GPUs to accelerate computation. However, Arm GPU security has not been explored by the community. Existing work has used Trusted Execution Environments (TEEs) to address GPU security concerns on Intel-based platforms, but there are numerous architectural differences that lead to novel technical challenges in deploying TEEs for Arm GPUs. There is a need for generalizable and efficient Arm-based GPU security mechanisms.To address these problems, we present STRONGBOX, the first GPU TEE for secured general computation on Arm endpoints. STRONGBOX provides an isolated execution environment by ensuring exclusive access to GPU. Our approach is based in part on a dynamic, fine-grained memory protection policy as Arm-based GPUs typically share a unified memory with the CPU. Furthermore, STRONGBOX reduces runtime overhead from the redundant security introspection operations. We also design an effective defense mechanism within secure world to protect the confidential GPU computation. Our design leverages the widely-deployed Arm TrustZone and generic Arm features, without hardware modification or architectural changes. We prototype STRONGBOX using an off-the-shelf Arm Mali GPU and perform an extensive evaluation. Results show that STRONGBOX successfully ensures GPU computation security with a low (4.70% -15.26%) overhead.

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Section: Related Workmentioning

confidence: 99%

Building a Lightweight Trusted Execution Environment for Arm GPUs

Wang,

Deng,

Ning

et al. 2024

IEEE Trans. Dependable and Secure Comput.

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“…However, when the task is moved out of the TEE to a GPU, the security is not guaranteed. While several methods (Hunt et al, 2020;Deng et al, 2022;Yudha et al, 2022) have tried to incorporate GPUs in TEE-based confidential computing, the performance gains of using GPUs in these methods are quite limited. Nvidia recently launched GPU TEE (e.g., H100) to enable TEE features at the hardware level, which will be tested in our future work.…”

Section: Integrity Guaranteementioning

confidence: 99%

TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

Alam,

Chen

2023

Front. Big Data

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IntroductionBig graphs like social network user interactions and customer rating matrices require significant computing resources to maintain. Data owners are now using public cloud resources for storage and computing elasticity. However, existing solutions do not fully address the privacy and ownership protection needs of the key involved parties: data contributors and the data owner who collects data from contributors.MethodsWe propose a Trusted Execution Environment (TEE) based solution: TEE-Graph for graph spectral analysis of outsourced graphs in the cloud. TEEs are new CPU features that can enable much more efficient confidential computing solutions than traditional software-based cryptographic ones. Our approach has several unique contributions compared to existing confidential graph analysis approaches. (1) It utilizes the unique TEE properties to ensure contributors' new privacy needs, e.g., the right of revocation for shared data. (2) It implements efficient access-pattern protection with a differentially private data encoding method. And (3) it implements TEE-based special analysis algorithms: the Lanczos method and the Nystrom method for efficiently handling big graphs and protecting confidentiality from compromised cloud providers.ResultsThe TEE-Graph approach is much more efficient than software crypto approaches and also immune to access-pattern-based attacks. Compared with the best-known software crypto approach for graph spectral analysis, PrivateGraph, we have seen that TEE-Graph has 103−105times lower computation, storage, and communication costs. Furthermore, the proposed access-pattern protection method incurs only about 10%-25% of the overall computation cost.DiscussionOur experimentation showed that TEE-Graph performs significantly better and has lower costs than typical software approaches. It also addresses the unique ownership and access-pattern issues that other TEE-related graph analytics approaches have not sufficiently studied. The proposed approach can be extended to other graph analytics problems with strong ownership and access-pattern protection.

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