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.
@article{arxiv.1909.09804,
title = {Challenges of Privacy-Preserving Machine Learning in IoT},
author = {Mengyao Zheng and Dixing Xu and Linshan Jiang and Chaojie Gu and Rui Tan and Peng Cheng},
journal= {arXiv preprint arXiv:1909.09804},
year = {2019}
}
Comments
In First International Workshop on Challenges in Artificial Intelligence and Machine Learning (AIChallengeIoT'19) November 10-13, 2019. 7 pages