Logistic regression model training based on the approximate homomorphic encryption.
- Published in:
- BMC Medical Genomics, 2018, v. 11, n. 4, p. N.PAG, doi. 10.1186/s12920-018-0401-7
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- Publication type:
- Article
Works matching IS 17558794 AND DT 2018 AND VI 11 AND IP 4
Results: 6
1
2
Privacy-preserving record linkage in large databases using secure multiparty computation.
- Published in:
- BMC Medical Genomics, 2018, v. 11, n. 4, p. N.PAG, doi. 10.1186/s12920-018-0400-8
- By:
- Publication type:
- Article
3
Secure top most significant genome variants search: iDASH 2017 competition.
- Published in:
- BMC Medical Genomics, 2018, v. 11, n. 4, p. N.PAG, doi. 10.1186/s12920-018-0399-x
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- Publication type:
- Article
4
Privacy-preserving logistic regression training.
- Published in:
- BMC Medical Genomics, 2018, v. 11, n. 4, p. N.PAG, doi. 10.1186/s12920-018-0398-y
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- Publication type:
- Article
5
Logistic regression over encrypted data from fully homomorphic encryption.
- Published in:
- BMC Medical Genomics, 2018, v. 11, n. 4, p. N.PAG, doi. 10.1186/s12920-018-0397-z
- By:
- Publication type:
- Article
6
iDASH secure genome analysis competition 2017.
- Published in:
- BMC Medical Genomics, 2018, v. 11, n. 4, p. N.PAG, doi. 10.1186/s12920-018-0396-0
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- Publication type:
- Article