Multi-objective Optimization of Vehicle Routing with Environmental Penalty. - Luis Bernardo Pulido-Gaytan; Andrei Tchernykh; Sergio Nesmachnow; Alfredo Cristóbal-Salas; Arutyun Avetisyan; Harold Enrique Castro Barrera; Carlos Jaime Barrios Hernández (ISUM, 2019)

A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption. - Luis Bernardo Pulido-Gaytan; Andrei Tchernykh; Jorge M. Cortés-Mendoza; Mikhail G. Babenko; Gleb I. Radchenko (CARLA, 2020)

Privacy-Preserving Logistic Regression as a Cloud Service Based on Residue Number System. - Jorge M. Cortés-Mendoza; Andrei Tchernykh; Mikhail G. Babenko; Luis Bernardo Pulido-Gaytan; Gleb I. Radchenko; Franck Leprévost; Xinheng Wang 001; Arutyun Avetisyan (RuSCDays, 2020)

LR-GD-RNS: Enhanced Privacy-Preserving Logistic Regression Algorithms for Secure Deployment in Untrusted Environments. - Jorge M. Cortés-Mendoza; Gleb I. Radchenko; Andrei Tchernykh; Bernardo Pulido-Gaytan; Mikhail G. Babenko; Arutyun Avetisyan; Pascal Bouvry; Albert Y. Zomaya (CCGRID, 2021)

RRNS Base Extension Error-Correcting Code for Performance Optimization of Scalable Reliable Distributed Cloud Data Storage. - Mikhail G. Babenko; Andrei Tchernykh; Luis Bernardo Pulido-Gaytan; Jorge M. Cortés-Mendoza; Egor M. Shiryaev; Elena Golimblevskaia; Arutyun Avetisyan; Sergio Nesmachnow (IPDPS Workshops, 2021)

Cryptographic Primitives Optimization Based on the Concepts of the Residue Number System and Finite Ring Neural Network. - Andrei Tchernykh; Mikhail G. Babenko; Bernardo Pulido-Gaytan; Egor M. Shiryaev; Elena Golimblevskaia; Arutyun Avetisyan; Nguyen Viet Hung; Jorge M. Cortés-Mendoza (OLA, 2021)

Algorithm for Constructing Modular Projections for Correcting Multiple Errors Based on a Redundant Residue Number System Using Maximum Likelihood Decoding. - Mikhail G. Babenko; Anton S. Nazarov; Andrei Tchernykh; Luis Bernardo Pulido-Gaytan; Jorge M. Cortés-Mendoza; Irina Vashchenko (Program. Comput. Softw., 2021)

Optimization of Neural Network Training for Image Recognition Based on Trigonometric Polynomial Approximation. - Nikolay A. Vershkov; Mikhail G. Babenko; Andrei Tchernykh; Bernardo Pulido-Gaytan; Jorge M. Cortés-Mendoza; Viktor Andreevich Kuchukov; Nataliya Nikolaevna Kuchukova (Program. Comput. Softw., 2021)

Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities. - Luis Bernardo Pulido-Gaytan; Andrei Tchernykh; Jorge M. Cortés-Mendoza; Mikhail G. Babenko; Gleb I. Radchenko; Arutyun Avetisyan; Alexander Yu. Drozdov (Peer-to-Peer Netw. Appl., 2021)

An Efficient Method for Comparing Numbers and Determining the Sign of a Number in RNS for Even Ranges. - Andrei Tchernykh; Mikhail G. Babenko; Egor M. Shiriaev; Bernardo Pulido-Gaytan; Jorge M. Cortés-Mendoza; Arutyun Avetisyan; Alexander Yu. Drozdov; Viktor Andreevich Kuchukov (Comput., 2022)

Toward Understanding Efficient Privacy-Preserving Homomorphic Comparison. - Bernardo Pulido-Gaytan; Andrei Tchernykh; Franck Leprévost; Pascal Bouvry; Alfredo Goldman (IEEE Access, 2023)

A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption. - Mikhail G. Babenko; Elena Golimblevskaia; Andrei Tchernykh; Egor M. Shiriaev; Tatiana Ermakova; Luis Bernardo Pulido-Gaytan; Georgii V. Valuev; Arutyun Avetisyan; Lana A. Gagloeva (Big Data Cogn. Comput., 2023)

Enhancing Cloud Security through Efficient Polynomial Approximations for Homomorphic Evaluation of Neural Network Activation Functions. - Bernardo Pulido-Gaytan; Andrei Tchernykh; Mikhail G. Babenko; Jorge M. Cortés-Mendoza; Horacio González-Vélez; Arutyun Avetisyan (CCGridW, 2024)

Efficient Privacy-Preserving Convolutional Neural Networks with CKKS-RNS for Encrypted Image Classification. - Andrei Tchernykh; Marianne Salgado-Ramos; Bernardo Pulido-Gaytan; Horacio González-Vélez; Esteban Mosckos; Mikhail G. Babenko (IPDPS (Workshops), 2025)