Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms
This addresses privacy concerns for medical data platforms, but it is incremental as it builds on existing distributed and privacy-preserving methods.
The paper tackles the problem of patient data leakage in medical platforms by proposing a distributed deep learning framework that separates hidden layers, keeping the first layer locally to preserve privacy and using a centralized server for other layers to improve learning performance by leveraging all data from each platform during training.
This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.