TL;DR
This paper proposes a hybrid deep learning architecture that splits neural network processing between IoT devices and the cloud, enhancing privacy and efficiency through Siamese fine-tuning to minimize sensitive data exposure.
Contribution
It introduces a novel hybrid approach with Siamese fine-tuning for privacy-preserving, cooperative deep neural network inference on IoT devices and cloud systems.
Findings
Significant reduction in sensitive information exposed to the cloud.
Low local inference cost on modern smartphones.
Effective balance between privacy, utility, and performance.
Abstract
Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to…
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