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1
LR-GD-RNS: Enhanced Privacy-Preserving Logistic Regression ..:
, In:
2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
,
Cortes-Mendoza, Jorge M.
;
Radchenko, Gleb
;
Tchernykh, Andrei
... - p. 770-775 , 2021
Link:
https://doi.org/10.1109/CCGrid51090.2021.00093
RT T1 <cTitle>2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)</cTitle>: T1 <Title>LR-GD-RNS: Enhanced Privacy-Preserving Logistic Regression Algorithms for Secure Deployment in Untrusted Environments</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9499569&Exemplar=1&LAN=DE A1 Cortes-Mendoza, Jorge M. A1 Radchenko, Gleb A1 Tchernykh, Andrei A1 Pulido-Gaytan, Bernardo A1 Babenko, Mikhail A1 Avetisyan, Arutyun A1 Bouvry, Pascal A1 Zomaya, Albert YR 2021 K1 Training K1 Privacy K1 Genomics K1 Approximation algorithms K1 Prediction algorithms K1 Classification algorithms K1 Encryption K1 logistic regression K1 residue number system K1 homomorphic encryption K1 cloud security SP 770 OP 775 LK http://dx.doi.org/https://doi.org/10.1109/CCGrid51090.2021.00093 DO https://doi.org/10.1109/CCGrid51090.2021.00093 SF ELIB - SuUB Bremen
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