Verifiable and Confidential DNN Inference on Low-End Edge Devices (opens in new tab) 聽馃敀Hardware Security 聽Content type: Academic
Deploying deep neural network (DNN) inference on low-end edge devices raises two key challenges: protecting model confidentiality against a potentially compromised edge system and enabling verifiable inference without incurring prohibitive overhead. Existing approaches either house partial models and inference software within trusted execution environments (TEEs), resulting in high cost and an application-dependent trusted computing base (TCB), ...
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