Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation

BACKGROUND: Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve...

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Detalles Bibliográficos
Autores principales: Kim, Miran, Song, Yongsoo, Wang, Shuang, Xia, Yuhou, Jiang, Xiaoqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930176/
https://www.ncbi.nlm.nih.gov/pubmed/29666041
http://dx.doi.org/10.2196/medinform.8805

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930176/
https://www.ncbi.nlm.nih.gov/pubmed/29666041
http://dx.doi.org/10.2196/medinform.8805

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