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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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| Pubblicato in: | JMIR Med Inform |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
JMIR Publications
2018
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| Soggetti: | |
| 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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