Challenges of Privacy-Preserving Machine Learning in IoT
This addresses privacy issues for IoT systems, but it is incremental as it builds on existing methods and focuses on a specific application.
The paper tackles the problem of privacy concerns in IoT data processing by discussing challenges in applying existing privacy-preserving machine learning methods and presenting a lightweight neural network approach for data obfuscation at IoT objects, with evaluation on the MNIST dataset showing satisfactory performance.
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.