A Decentralized Machine Learning Scheme with Input Perturbation-Based Differential Privacy
Masakazu Okamoto, Koya Sato, Keiichi Iwamura · 2022
With the increase in Internet of Things (IoT) devices, machine learning and big data analysis have been rapidly developing. The current big data analysis based on deep learning assumes that the raw data owned by countless users is aggregated into the cloud. However, the number of applications requiring data containing personal information, such as healthcare, increases every year. There are many concerns about operating a system that assumes data aggregation to the cloud due to risks such as data leakage.