Privacy-Preserving Multi-Party Machine Learning for Object Detection

Abstract

In order to mitigate the privacy threats and resource constraints for real-time object detection applications on edge nodes, we describe an approach to building a distributed multi-party You Only Look Once object detector. We carefully separate out what each device can see to prevent the sharing of sensitive data and model whilst improving prediction results. Privacy, correctness and latency concerns were discussed along the paper showing that the approach does not leak sensitive information, enables the construction of machine learning models that are better than purely local models and where the overall performances are on par with the global predictions resulting from the pooling of all data.