Google. https://cloud.google.com/products/ai. Accessed 13 Mar 2020
Microsoft. https://azure.microsoft.com/en-us/services/machine-learning. Accessed 13 Mar 2020
Amazon. https://aws.amazon.com/machine-learning. Accessed 13 Mar 2020
CSO. https://www.csoonline.com/article/3441477/enabling-public-but-secure-deep-lea rning.html. Accessed 13 Mar 2020
PALISADE. https://palisade-crypto.org/community. Accessed 13 Mar 2020
Halevi, S., Shoup, V.: Algorithms in HElib. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014, Part I. LNCS, vol. 8616, pp. 554–571. Springer, Heidelberg (2014). https://doi.org/10. 1007/978-3-662-44371-2_31
HEANN. https://github.com/snucrypto/HEAAN. Accessed 13 Mar 2020
SEAL. https://github.com/Microsoft/SEAL. Accessed 13 Mar 2020
Chervyakov, N., Babenko, M., Tchernykh, A., Kucherov, N., Miranda-López, V., Cortés-Mendoza, J.M.: AR-RRNS: configurable reliable distributed data storage systems for Internet of Things to ensure security. Futur. Gener. Comput. Syst. 92, 1080–1092 (2019). https://doi. org/10.1016/j.future.2017.09.061
Aono, Y., Hayashi, T., Trieu Phong, L., Wang, L.: Scalable and secure logistic regression via homomorphic encryption. In: Proceedings of the Sixth ACM on Conference on Data and Application Security and Privacy-CODASPY 2016, pp. 142–144. ACM Press, New York (2016). https://doi.org/10.1145/2857705.2857731
Kim, A., Song, Y., Kim, M., Lee, K., Cheon, J.H.: Logistic regression model training based on the approximate homomorphic encryption. BMC Med. Genomics 11, 83 (2018)
Cheon, J.H., Han, K., Kim, A., Kim, M., Song, Y.: A full RNS variant of approximate homomorphic encryption. In: Cid, C., Jacobson, Jr. M. (eds.) Selected Areas in Cryptography – SAC 2018. LNCS, vol. 11349, pp. 347 − 368. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10970-7_16
Cheon, J.H., Kim, D., Kim, Y., Song, Y.: Ensemble method for privacy-preserving logistic regression based on homomorphic encryption. IEEE Access 6, 46938–46948 (2018)
Yoo, J.S., Hwang, J.H., Song, B.K., Yoon, J.W.: A bitwise logistic regression using binary approximation and real number division in homomorphic encryption scheme. In: Heng, S.-H., Lopez, J. (eds.) ISPEC 2019. LNCS, vol. 11879, pp. 20–40. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34339-2_2
Tchernykh, A., et al.: Towards mitigating uncertainty of data security breaches and collusion in cloud computing. In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA), pp. 137–141. IEEE (2017). https://doi.org/10.1109/DEXA.2017.44
Tchernykh, A., et al.: Performance evaluation of secret sharing schemes with data recovery in secured and reliable heterogeneous multi-cloud storage. Cluster Comput. 22(4), 1173–1185 (2019). https://doi.org/10.1007/s10586-018-02896-9
Babenko, M., et al.: Unfairness correction in P2P grids based on residue number system of a special form. In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA), pp. 147–151. IEEE (2017)
Babenko, M., et al.: Positional characteristics for efficient number comparison over the homomorphic encryption. Program. Comput. Softw. 45(8), 532–543 (2019). https://doi.org/10. 1134/S0361768819080115
Tchernykh, A., et al.: AC-RRNS: anti-collusion secured data sharing scheme for cloud storage. Int. J. Approx. Reason. 102, 60–73 (2018). https://doi.org/10.1016/j.ijar.2018.07.010
Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., Johannes, R.S.: Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, p. 261 (1988)
Tchernykh, A., et al.: Scalable data storage design for non-stationary IoT environment with adaptive security and reliability. IEEE Internet Things J. 7, 1 (2020). https://doi.org/10.1109/JIOT.2020.2981276