| Title | Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Jeon, Joohyung, Kim, Junhui, Kim, Joongheon, Kim, Kwangsoo, Mohaisen, Aziz, Kim, Jong-Kook |
| Conference Name | 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks – Supplemental Volume (DSN-S) |
| Keywords | Big Data, big data privacy, Biomedical imaging, Computational modeling, Data models, data privacy, Deep Learning, distributed deep learning framework, geo-distributed medical Big-data platforms, hidden layers, human factors, learning (artificial intelligence), local platforms, Medical Big Data, medical information systems, medical platforms, Metrics, privacy preserving, privacy-preserving deep learning computation, privacy-preserving medical data training, pubcrawl, Resiliency, Scalability, Servers, Training |
| 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. |
| DOI | 10.1109/DSN-S.2019.00007 |
| Citation Key | jeon_privacy-preserving_2019 |
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms