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Title: Lightweight and unobtrusive data obfuscation at IoT edge for remote inference
Authors: Xu, Dixing
Zheng, Mengyao
Jiang, Linshan
Gu, Chaojie
Tan, Rui
Cheng, Peng
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Xu, D., Zheng, M., Jiang, L., Gu, C., Tan, R. & Cheng, P. (2020). Lightweight and unobtrusive data obfuscation at IoT edge for remote inference. IEEE Internet of Things Journal, 7(10), 9540-9551.
Project: NTU-SUG 
Journal: IEEE Internet of Things Journal 
Abstract: Executing deep neural networks for inference on the server-class or cloud backend based on the data generated at the edge of the Internet of Things is desirable due primarily to the limited compute power of the edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This article presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free-spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving backend's inference accuracy.
ISSN: 2327-4662
DOI: 10.1109/JIOT.2020.2983278
Schools: School of Computer Science and Engineering 
Research Centres: HP-NTU Digital Manufacturing Corporate Lab
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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