A design study showing how TrustZone and model partitioning could keep deep learning models secret on untrusted phones, with a rough fourfold overhead estimate for a small network.
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Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices
A design study showing how TrustZone and model partitioning could keep deep learning models secret on untrusted phones, with a rough fourfold overhead estimate for a small network.