21 citations · 97 across the 16 of their papers we have counts for
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cs.CR2021★ 1 cited
Memory-Efficient Deep Learning Inference in Trusted Execution Environments
Jean-Baptiste Truong, William Gallagher, Tian Guo +1
This study identifies and proposes techniques to alleviate two key bottlenecks to executing deep neural networks in trusted execution environments (TEEs): page thrashing during the…
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…