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We present DarkneTZ, a framework that uses an edge device's Trusted ExecutionEnvironment (TEE) in conjunction with model partitioning to limit the attacksurface against Deep Neural Networks (DNNs). Increasingly, edge devices(smartphones and consumer IoT devices) are equipped with pre-trained DNNs for avariety of applications. This trend comes with privacy risks as models can leakinformation about their training data through effective membership inferenceattacks (MIAs). We evaluate the performance of DarkneTZ, including CPUexecution time, memory usage, and accurate power consumption, using two smalland six large image classification models. Due to the limited memory of theedge device's TEE, we partition model layers into more sensitive layers (to beexecuted inside the device TEE), and a set of layers to be executed in theuntrusted part of the operating system. Our results show that even if a singlelayer is hidden, we can provide reliable model privacy and defend against stateof the art MIAs, with only 3% performance overhead. When fully utilizing theTEE, DarkneTZ provides model protections with up to 10% overhead.

DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments