In the emerging Internet of Things (IoT) paradigm, mobile cloud inference serves as an efficient application framework that relieves the computation and storage burden on resource-constrained mobile devices by offloading the workload to cloud servers. However, mobile cloud inference encounters computation, communication, and privacy challenges to ensure efficient system inference and protect the privacy of mobile users’ collected information. To address the deployment of deep neural networks (DNN) with large capacity, we propose splitting computing (SC) where the entire model is divided into two parts, to be executed on mobile and cloud ends respectively. However, the transmission of intermediate data poses a bottleneck to system performance. This paper initially demonstrates the privacy issue arising from the machine analysis-oriented intermediate feature. We conduct a preliminary experiment to intuitively reveal the latent potential for enhancing the privacy-preserving ability of the initial feature. Motivated by this, we propose a framework for privacy-preserving intermediate feature compression, which addresses the limitations in both compression and privacy that arise in the original extracted feature data. Specifically, we propose a method that jointly enhances privacy and encoding efficiency, achieved through the collaboration of the encoding feature privacy enhancement module and the privacy feature ordering enhancement module. Additionally, we develop a gradient-reversal optimization strategy based on information theory to ensure the utmost concealment of core privacy information throughout the entire codec process. We evaluate the proposed method on two DNN models using two datasets, demonstrating its ability to achieve superior analysis accuracy and higher privacy preservation than HEVC. Furthermore, we provide an application case of a wireless sensor network to validate the effectiveness of the proposed method in a real-world scenario.
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