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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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Dettagli Bibliografici
Pubblicato in:JMIR Med Inform
Autori principali: Kim, Miran, Song, Yongsoo, Wang, Shuang, Xia, Yuhou, Jiang, Xiaoqian
Natura: Artigo
Lingua:Inglês
Pubblicazione: JMIR Publications 2018
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Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC5930176/
https://ncbi.nlm.nih.gov/pubmed/29666041
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.2196/medinform.8805
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