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Executing deep neural networks for inference on the server-class or cloudbackend based on data generated at the edge of Internet of Things is desirabledue primarily to the limited compute power of edge devices and the need toprotect the confidentiality of the inference neural networks. However, such aremote inference scheme incurs concerns regarding the privacy of the inferencedata transmitted by the edge devices to the curious backend. This paperpresents a lightweight and unobtrusive approach to obfuscate the inference dataat the edge devices. It is lightweight in that the edge device only needs toexecute a small-scale neural network; it is unobtrusive in that the edge devicedoes not need to indicate whether obfuscation is applied. Extensive evaluationby three case studies of free spoken digit recognition, handwritten digitrecognition, and American sign language recognition shows that our approacheffectively protects the confidentiality of the raw forms of the inference datawhile effectively preserving the backend's inference accuracy.

Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference