Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption
- DOI
- 10.1186/s12920-020-0723-0
- Published
- 2020-07
- Container
- BMC Medical Genomics
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s12920-020-0723-0,
title = {Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption},
author = {Sergiu Carpov and Nicolas Gama and Mariya Georgieva and Juan Ramon Troncoso-Pastoriza},
year = {2020},
journal = {BMC Medical Genomics},
doi = {10.1186/s12920-020-0723-0},
url = {https://doi.org/10.1186/s12920-020-0723-0}
}RIS
TY - JOUR TI - Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption AU - Sergiu Carpov AU - Nicolas Gama AU - Mariya Georgieva AU - Juan Ramon Troncoso-Pastoriza PY - 2020 JO - BMC Medical Genomics DO - 10.1186/s12920-020-0723-0 UR - https://doi.org/10.1186/s12920-020-0723-0 ER -
APA
Carpov, S., Gama, N., Georgieva, M., & Troncoso-Pastoriza, J. R. (2020). Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption. BMC Medical Genomics. https://doi.org/10.1186/s12920-020-0723-0
Source records
- crossref · retrieved 2026-11-10T12:49:55.036Z