Abstract
Supporting the inference tasks of convolutional neural network (CNN) on resource-constrained Internet of Things (IoT) devices in a timely manner has been an outstanding challenge for emerging smart systems. To mitigate the burden on IoT devices, one prevalent solution is to offload the CNN inference tasks to the public cloud. However, this “offloading-to-cloud” solution may cause privacy breach since the offloaded data can contain sensitive information. For privacy protection, the research community has resorted to advanced cryptographic primitives to support CNN inference over encrypted data. Nevertheless, these attempts are limited by the real-time performance due to the heavy IoT computational overhead brought by cryptographic primitives.
In this paper, we propose an edge-computing-assisted scheme to boost the efficiency of CNN inference tasks on IoT devices, which also protects the privacy of IoT data to be offloaded. In our scheme, the most time-consuming convolutional and fully-connected layers are offloaded to edge computing devices and the IoT device only performs efficient encryption and decryption on the fly. As a result, our scheme enables IoT devices to securely offload over 99% CNN operations, and edge devices to execute CNN inference over encrypted data as efficiently as on plaintext. Experiments on AlexNet show that our scheme can speed up CNN inference for more than \(35\times \) with a 95.56% energy saving for IoT devices.
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Tian, Y., Yuan, J., Yu, S., Hou, Y., Song, H. (2019). Edge-Assisted CNN Inference over Encrypted Data for Internet of Things. In: Chen, S., Choo, KK., Fu, X., Lou, W., Mohaisen, A. (eds) Security and Privacy in Communication Networks. SecureComm 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 304. Springer, Cham. https://doi.org/10.1007/978-3-030-37228-6_5
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