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20192021
most citedConfidential Deep Learning: Executing Proprietary Models on Untrusted Devices

18 citations · 30 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CR2019

DRAB-LOCUS: An Area-Efficient AES Architecture for Hardware Accelerator Co-Location on FPGAs

Jacob T. Grycel, Robert J. Walls

Advanced Encryption Standard (AES) implementations on Field Programmable Gate Arrays (FPGA) commonly focus on maximizing throughput at the cost of utilizing high volumes of FPGA sl…

cs.CR2019★ 2 cited

Silhouette: Efficient Protected Shadow Stacks for Embedded Systems

Jie Zhou, Yufei Du, Zhuojia Shen +3

Microcontroller-based embedded systems are increasingly used for applications that can have serious and immediate consequences if compromised---including automobile control systems…

cs.CR2019★ 18 cited

Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices

Peter M. VanNostrand, Ioannis Kyriazis, Michelle Cheng +2

Performing deep learning on end-user devices provides fast offline inference results and can help protect the user's privacy. However, running models on untrusted client devices re…

cs.CR2019★ 1 cited

ERHARD-RNG: A Random Number Generator Built from Repurposed Hardware in Embedded Systems

Jacob Grycel, Robert J. Walls

Quality randomness is fundamental to cryptographic operations but on embedded systems good sources are (seemingly) hard to find. Rather than use expensive custom hardware, our ERHA…

cs.PF2019★ 4 cited

Speeding up Deep Learning with Transient Servers

Shijian Li, Robert J. Walls, Lijie Xu +1

Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such sp…