Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 17.04.18 in Vol 6, No 2 (2018): Apr-Jun

This paper is in the following e-collection/theme issue:

Works citing "Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation"

According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.8805):

(note that this is only a small subset of citations)

  1. Yang Q, Liu Y, Chen T, Tong Y. Federated Machine Learning. ACM Transactions on Intelligent Systems and Technology 2019;10(2):1
    CrossRef
  2. Kim A, Song Y, Kim M, Lee K, Cheon JH. Logistic regression model training based on the approximate homomorphic encryption. BMC Medical Genomics 2018;11(S4)
    CrossRef
  3. Cheon JH, Kim D, Kim Y, Song Y. Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption. IEEE Access 2018;6:46938
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/medinform.8805)

:
  1. Cheon JH, Han K, Kim A, Kim M, Song Y. Selected Areas in Cryptography – SAC 2018. 2019. Chapter 16:347
    CrossRef
  2. Bergamaschi F, Halevi S, Halevi TT, Hunt H. Applied Cryptography and Network Security. 2019. Chapter 29:592
    CrossRef
  3. Kim D, Song Y. Information Security and Cryptology – ICISC 2018. 2019. Chapter 6:85
    CrossRef