Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms

• URL: http://arxiv.org/abs/2001.02932v1
• Date: Thu, 9 Jan 2020 11:46:29 GMT
• Title: Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms
• Authors: Joohyung Jeon, Junhui Kim, Joongheon Kim, Kwangsoo Kim, Aziz Mohaisen, and Jong-Kook Kim
• Abstract summary: 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.
• Score: 12.64748421991378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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