19877

Telekine: Secure Computing with Cloud GPUs

Tyler Hunt, Zhipeng Jia, Vance Miller, Ariel Szekely, Yige Hu, Christopher J. Rossbach, Emmett Witchel
The University of Texas at Austin
17th USENIX Symposium on Networked Systems Design and Implementation (NSDI’20), 2020

@article{hunt2020telekine,

   title={Telekine: Secure Computing with Cloud GPUs},

   author={Hunt, Tyler and Jia, Zhipeng and Miller, Vance and Szekely, Ariel and Hu, Yige and Rossbach, Christopher J and Witchel, Emmett},

   year={2020}

}

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GPUs have become ubiquitous in the cloud due to the dramatic performance gains they enable in domains such as machine learning and computer vision. However, offloading GPU computation to the cloud requires placing enormous trust in providers and administrators. Recent proposals for GPU trusted execution environments (TEEs) are promising but fail to address very real side-channel concerns. To illustrate the severity of the problem, we demonstrate a novel attack that enables an attacker to correctly classify images from ImageNet [17] by observing only the timing of GPU kernel execution, rather than the images themselves. Telekine enables applications to use GPU acceleration in the cloud securely, based on a novel GPU stream abstraction that ensures execution and interaction through untrusted components are independent of any secret data. Given a GPU with support for a TEE, Telekine employs a novel variant of API remoting to partition application-level software into components to ensure secret-dependent behaviors occur only on trusted components. Telekine can securely train modern image recognition models on MXNet [10] with 10%–22% performance penalty relative to an insecure baseline with a locally attached GPU. It runs graph algorithms using Galois [75] on one and two GPUs with 18%–41% overhead.
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