Publications

Journals

• B. Pulido-Gaytan, A. Tchernykh. Self-Learning Activation Functions to Increase Accuracy of Privacy-Preserving Convolutional Neural Networks with Homomorphic Encryption. PLoS ONE 19(7): e0306420 (2024). 10.1371/journal.pone.0306420
• B. Pulido-Gaytan, A. Tchernykh, F. Leprévost, P. Bouvry, A. Goldman. Towards Understanding Efficient Privacy-Preserving Homomorphic Comparison. IEEE Access. 11, 102189-102206 (2023). 10.1109/ACCESS.2023.3315655
• M. Babenko, E. Golimblevskaia, A. Tchernykh, E. Shiriaev, T. Ermakova, B. Pulido-Gaytan, G. Valuev, A. Avetisyan, L. Gagloeva. A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption. Big Data Cogn. Comput. 7(2), 84 (2023). 10.3390/bdcc7020084
• E. Bezuglova, E. Shiriaev, M. Babenko, A. Tchernykh, B. Pulido-Gaytan, J.M. Cortes-Mendoza. A survey on multi-cloud storage security: threats and countermeasures. Computational Technologies. 28(1), 72-80 (2023). 10.25743/ICT.2023.28.1.008
• M. Babenko, A. Tchernykh, B. Pulido-Gaytan, A. Avetisyan, S. Nesmachnow, X. Wang, F. Granelli. Towards the sign function best approximation for secure outsourced computations and control. Mathematics. 10(12), 2006 (2022). 10.3390/math10122006
• RM. Canosa-Reyes, A. Tchernykh, JM. Cortes-Mendoza, B. Pulido-Gaytan, R. Rivera-Rodriguez, JE. Lozano-Rizk, et al. Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds. PLoS ONE 17(1), e0261856 (2022). 10.1371/journal.pone.0261856
• A. Tchernykh, M. Babenko, E. Shiriaev, B. Pulido-Gaytan, JM. Cortes-Mendoza, A. Avetisyan, AY. Drozdov, V. Kuchukov. An Efficient Method for Comparing Numbers and Determining the Sign of a Number in RNS for Even Ranges. Computation 10(2), 17 (2022). 10.3390/computation10020017
• B. Pulido-Gaytan, A. Tchernykh, J. M. Cortes-Mendoza, M. Babenko, G. Radchenko, A. Avetisyan, A. Yu. Drozdov. Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities. Peer-to-Peer Netw. Appl. 14, 1666 (2021). 10.1007/s12083-021-01076-8
• N. Vershkov, M. Babenko, A. Tchernykh, B. Pulido-Gaytan, J. M. Cortes-Mendoza, V. Kuchukova, N. Kuchukov. Optimization of Neural Network Training for Image Recognition Based on Trigonometric Polynomial Approximation. Program Comput Soft 47, 830–838 (2021). 10.1134/S0361768821080272
• M. Babenko, A. Nazarova, A. Tchernykh, B. Pulido-Gaytan, J. M. Cortes-Mendoza, I. Vashchenko. Algorithm for Constructing Modular Projections for Correcting Multiple Errors Based on a Redundant Residue Number System Using Maximum Likelihood Decoding. Program Comput Soft 47, 839–848 (2021). 10.1134/S0361768821080089

Conferences

• M. A. Ojewale, A. E. Chis, J. M. Cortes-Mendoza, B. Pulido-Gaytan, H. Gonzalez-Velez. FlashbackCL: Mitigating Temporal Forgetting in Federated Learning. 35th International Joint Conference on Artificial Intelligence (IJCAI 2026), 15-21 August, 2026, Bremen, Germany (CORE Rank A^*)(Accepted).
• B. Pulido-Gaytan, A. Tchernykh, H. González-Vélez. Cryptographically Computable Self-Learning Activation Functions for Privacy-Preserving Logistic Regression with Homomorphic Encryption. 40th IEEE International Parallel & Distributed Processing Symposium (IPDPS), New Orleans, LA, USA, 2026, pp. 571-579, 10.1109/IPDPSW71298.2026.00096(CORE Rank A)
• A. G. Calzada-Jasso, A. Tchernykh, I. D. Avendano-Pacheco, J. M. Cortes-Mendoza, B. Pulido-Gaytan, M. Babenko, A. Goldman, H. González-Vélez. Generative Fabrication of Medical Images for Machine Learning Training. 37th IEEE/SBC International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Bonito, MS, Brazil, 2025, pp. 136-145, 10.1109/SBAC-PAD66369.2025.00022 (CORE Rank B).
• A. Tchernykh, M. Salgado-Ramos, B. Pulido-Gaytan, H. González-Vélez, E. Mocskos, M. Babenko. Efficient Privacy-Preserving Convolutional Neural Networks with CKKS-RNS for Encrypted Image Classification. 39th IEEE International Parallel and Distributed Processing Symposium (IPDPS), Milano, Italy, 2025, pp. 343-352, 10.1109/IPDPSW66978.2025.00059(CORE Rank A)
• B. Pulido-Gaytan, A. Tchernykh, M. Babenko, J. M. Cortes-Mendoza, H. González-Vélez, A. Avetisyan. Enhancing Cloud Security through Efficient Polynomial Approximations for Homomorphic Evaluation of Neural Network Activation Functions. 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), Philadelphia, PA, USA, 2024, pp. 42-49, 10.1109/CCGridW63211.2024.00011(CORE Rank A)
• E. Shiryaev, E. Bezuglova, M. Babenko, A. Tchernykh, B. Pulido-Gaytan, J. M. Cortes-Mendoza. Performance Impact of Error Correction Codes in RNS with Returning Methods and Base Extension. 2021 International Conference Engineering and Telecommunication, 2021, pp. 1-5, 10.1109/EnT50460.2021.9681756
• J. M. Cortes-Mendoza, A. Tchernykh, M. Babenko, B. Pulido-Gaytan, G. Radchenko. Multi-cloud Privacy-Preserving Logistic Regression. In: Voevodin V., Sobolev S. (eds) Supercomputing. RuSCDays 2021. vol 1510. pp 457-471, Springer, 10.1007/978-3-030-92864-3_35
• A. Tchernykh, N. Babenko, B. Pulido-Gaytan, E. Shiryaev, E. Golimblevskaia, A. Avetisyan, N. Viet Hung, JM. Cortes-Mendoza. Cryptographic Primitives Optimization Based on the Concepts of the Residue Number System and Finite Ring Neural Network. In: Dorronsoro B., Amodeo L., Pavone M., Ruiz P. (eds) Optimization and Learning. OLA 2021. Communications in Computer and Information Science, vol 1443. pp. 241-253, Springer, 2021, 10.1007/978-3-030-85672-4_18
• M. Babenko, A. Tchernykh, B. Pulido-Gaytan, JM. Cortes-Mendoza, E. Shiryaev, E. Golimblevskaia, A. Avetisyan, S. Nesmachnow. RRNS Base Extension Error-Correcting Code for Performance Optimization of Scalable Reliable Distributed Cloud Data Storage. IPDPSW 2021 - IEEE International Parallel and Distributed Processing Symposium Workshops, 548-553, 2021, 10.1109/IPDPSW52791.2021.00087 (CORE Rank A)
• JM. Cortes-Mendoza, G. Radchenko, A. Tchernykh, B. Pulido-Gaytan, M. Babenko, A. Avetisyan, P. Bouvry, A. Zomaya. LR-GD-RNS: Enhanced Privacy-Preserving Logistic Regression Algorithms for Secure Deployment in Untrusted Environments. CCGRID 2021-The 21th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, 2021, pp. 770-775, Melbourne, Victoria, Australia, 10.1109/CCGrid51090.2021.00093 (CORE Rank A)
• A. Tchernykh, A. Facio Medina, B. Pulido-Gaytan, JM. Cortes-Mendoza, G. Radchenko, M. Babenko, I. Chernykh, I. Kulikov, S. Nesmachnow, R. Rivera-Rodriguez. Toward digital twins' workload allocation on clouds with low-cost microservices streaming interaction. ISPRAS OPEN 2020 - Ivannikov ISP RAS Open Conference, Moscow, Russia, December 10-11, 2020 pp. 115-124, 10.1109/ISPRAS51486.2020.00024
• M. Babenko, A. Tchernykh, B. Pulido-Gaytan, E. Golimblevskaia, J. M. Cortes-Mendoza, A. Avetisyan. Experimental Evaluation of Homomorphic Comparison Methods, 2020 Ivannikov Ispras Open Conference (ISPRAS), 2020, pp. 69-74, 10.1109/ISPRAS51486.2020.00017
• M. Babenko, A. Tchernykh, E. Golimblevskaia, B. Pulido-Gaytan, A. Avetisyan. Homomorphic Comparison Methods: Technologies, Challenges, and Opportunities. En&T-2020 - 2020 International Conference Engineering and Telecommunication, IEEE, 2021, pp. 1-5, 10.1109/EnT50437.2020.9431252
• E. Shiryaev, E. Golimblevskaia, M. Babenko, A. Tchernykh. Improvement of the approximate method for the comparison operation in the RNS. En&T-2020 - 2020 International Conference Engineering and Telecommunication (En&T), IEEE, 2021, pp. 1-6, 10.1109/EnT50437.2020.9431290
• J. M. Cortes-Mendoza, A. Tchernykh, M. Babenko, B. Pulido-Gaytan, G. Radchenko, F. Leprevost, X. Wang. Privacy-Preserving Logistic Regression as a Cloud Service based on Residue Number System. In Supercomputing. RuSCDays 2020. vol 1331, 10.1007/978-3-030-64616-5_51
• B. Pulido-Gaytan, A. Tchernykh, JM. Cortes-Mendoza, M. Babenko, G. Radchenko. A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption. In: In: Nesmachnow S., Castro H., Tchernykh A. (eds) High Performance Computing. CARLA 2020. vol 1327. pp 115-129 2021, Springer, 10.1007/978-3-030-68035-0_9
• B. Pulido-Gaytan, A. Tchernykh, S. Nesmachnow, A. Salas, A. Avetisyan, H. E. Castro, C. J. Barrios. Multi-Objective Optimization of Vehicle Routing with Environmental Penalty. In: Torres M., Klapp J. (eds) Supercomputing. ISUM 2019. Communications in Computer and Information Science, vol 1151, pp 147-162, 2019, Springer, 10.1007/978-3-030-38043-4_13
• B. Pulido-Gaytan, A. Tchernykh, M. Babenko, J. M. Cortes-Mendoza, G. Radchenko, A. Avetisyan, A. Yu. Drozdov. Toward Fast and Accurate Polynomial Approximations for Practical Homomorphic Evaluation of Neural Network Activation Functions. HPCS 2020 - The 18th International Conference on High Performance Computing & Simulation. October 26-30, 2020, Barcelona, Spain (CORE Rank B) (Accepted)

Theses

• B. Pulido-Gaytan, “Self-learning activation functions for privacy-preserving convolutional neural networks with homomorphic encryption.” Ph.D. Thesis, CICESE Research Center, Mexico, 2024. [URL]
• B. Pulido-Gaytan, “Multi-Objective Optimization of Vehicle Routing with Environmental Penalty in Smart Cities.” M.Sc. Thesis, CICESE Research Center, Mexico, 2019. [URL]