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arXiv · 1909.09804

Challenges of Privacy-Preserving Machine Learning in IoT

Mengyao Zheng·Dixing Xu·Linshan Jiang·Chaojie Gu·Rui Tan·Peng Cheng
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Abstract

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches developed in the context of cloud computing and discusses the challenges of applying them in the context of IoT. Moreover, we present a privacy-preserving inference approach that runs a lightweight neural network at IoT objects to obfuscate the data before transmission and a deep neural network in the cloud to classify the obfuscated data. Evaluation based on the MNIST dataset shows satisfactory performance.

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Mengyao Zheng, Dixing Xu, Linshan Jiang, Chaojie Gu, Rui Tan, Peng Cheng. 2019-09-21. Challenges of Privacy-Preserving Machine Learning in IoT. https://doi.org/10.1145/3363347.3363357

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