Utilizing GPU Virtualization to Protect the Private Keys of GPU Cryptographic Computation

Wang, Ziyang; Zheng, Fangyu; Lin, Jingqiang; Dong, Jiankuo

doi:10.1007/978-3-030-01950-1_9

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…As discussed in [56], PixelVault is actually vulnerable to unprivileged attackers, however several authors suggested ways to enhance its approach. For instance, the authors in [52] propose to run CUDA cryptographic applications inside guest VMs by using a virtualized GPU; no privileged attacker on guest VMs is able to retrieve the encryption keys, because they are never stored in the guest VM memory. In [49] the authors suggest to modify the GPU hardware to prevent the device driver from directly accessing GPU critical internal resources.…”

Section: Related Workmentioning

confidence: 99%

MemShield: GPU-Assisted Software Memory Encryption

Santucci¹,

Ingrassia²,

Picierro

et al. 2020

Applied Cryptography and Network Security

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Cryptographic algorithm implementations are vulnerable to Cold Boot attacks, which consist in exploiting the persistence of RAM cells across reboots or power down cycles to read the memory contents and recover precious sensitive data. The principal defensive weapon against Cold Boot attacks is memory encryption. In this work we propose MemShield, a memory encryption framework for user space applications that exploits a GPU to safely store the master key and perform the encryption/decryption operations. We developed a prototype that is completely transparent to existing applications and does not require changes to the OS kernel. We discuss the design, the related works, the implementation, the security analysis, and the performances of MemShield.

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Section: Related Workmentioning

confidence: 99%