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Dirección de Estudios de Posgrado

Pulido Gaytan Luis Bernardo

Posgrado: Ciencias de la Computación
Nivel:Doctorado en Ciencias
Matrícula:20161390
País de origen:México
lpulido@cicese.edu.mx
Tesis

Título de tesis: Self-learning activation functions for privacy-preserving convolutional neural networks with homomorphic encryption

Fecha de defensa: Fecha por confirmar

Líneas de generación y aplicación: Cómputo Paralelo, Distribuido y Redes

Comité:

Andrey Chernykh - Director

Pedro Gilberto López Mariscal - Miembro de comité

Raúl Rivera Rodríguez - Miembro de comité

Mikhail Babenko - Miembro de comité

Publicaciones

Pulido Gaytan, L. B., Chernykh, A., Leprevost, F., Bouvry, P., & Goldman, A. (2023). Toward Understanding Efficient Privacy-Preserving Homomorphic Comparison. IEEE Access, 11, 102189-102206. doi: 10.1109/ACCESS.2023.3315655. (ID: 29291)
Babenko, M., Golimblevskaia, E., Chernykh, A., Shiriaev, E., Ermakova, T., Pulido Gaytan, L. B., VALUEV, G., Avetisyan, A., & Gagloeva, L. A. (2023). A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption. Big Data and Cognitive Computing, 7(2), 84. doi: 10.3390/bdcc7020084. (ID: 29301)
Babenko, M., Chernykh, A., Pulido Gaytan, L. B., Avetisyan, A., Nesmachnow , S., Wang, X., & Granelli, F. (2022). Towards the sign function best approximation for secure outsourced computations and control. Mathematics, 10(12), 2006. doi: 10.3390/math10122006. (ID: 28212)
Chernykh, A., Babenko, M., Shiriaev, E., Pulido Gaytan, L. B., Cortés Mendoza, J. M., Avetisyan, A., Drozdov, A. Y., & Kuchukov, V. (2022). An Efficient Method for Comparing Numbers and Determining the Sign of a Number in RNS for Even Ranges. Computation, 10(2), 17. doi: 10.3390/computation10020017. (ID: 27459)
Canosa Reyes, R. M., Chernykh, A., Cortés Mendoza, J. M., Pulido Gaytan, L. B., Rivera Rodríguez, R., Lozano Rizk, J. E., Concepción Morales, E., Castro Barrera, H. E., Barrios Hernandez, C. B., Medrano Jaimes, H. F., Avetisyan, A., Babenko, M., & Drozdov, A. Y. (2022). Dynamic performance¿Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds. PLoS ONE, 17(1), e0261856. doi: 10.1371/journal.pone.0261856. (ID: 27456)
Vershkov, N., Babenko, M., Chernykh, A., Pulido Gaytan, L. B., Cortés Mendoza, J. M., Kuchukov, V., & Kuchukova, N. N. (2021). Optimization of Neural Network Training for Image Recognition Based on Trigonometric Polynomial Approximation. PROGRAMMING AND COMPUTER SOFTWARE, 47(1), 830-838. doi: 10.1134/S0361768821080272. (ID: 27474)
Babenko, M., Nazarov, A., Chernykh, A., Pulido Gaytan, L. B., Cortés Mendoza, J. M., & Vashchenko, i. (2021). Algorithm for Constructing Modular Projections for Correcting Multiple Errors Based on a Redundant Residue Number System Using Maximum Likelihood Decoding. PROGRAMMING AND COMPUTER SOFTWARE, 47(1), 839-848. doi: 10.1134/S0361768821080089. (ID: 27479)
Shiryaev, E., Bezuglova, E., Babenko, M., Chernykh, A., Pulido Gaytan, L. B., & Cortés Mendoza, J. M. (2021, Noviembre). Performance impact of error correction codes in RNS with returning methods and base extension. International Conference Engineering and Telecommunication (En&T-2021). Dolgoprudnyy, Rusia (ID: 28262)
Cortés Mendoza, J. M., Chernykh, A., Babenko, M., Pulido Gaytan, L. B., & Radchenko, G. (2021, Septiembre). Multi-cloud Privacy-Preserving Logistic Regression. Russian Supercomputing Days 2021. Moscu, Rusia (ID: 27499)
Chernykh, A., Babenko, M., Pulido Gaytan, L. B., Shiryaev, E., Golimblevskaia, E., & Avetisyan, A. (2021, Agosto). Cryptographic Primitives Optimization Based on the Concepts of the Residue Number System and Finite Ring Neural Network. 2021 International Conference on Optimization and Learning. Catania, Italia (ID: 27502)
Babenko, M., Chernykh, A., Pulido Gaytan, L. B., Cortés Mendoza, J. M., Shiryaev, E., Golimblevskaia, E., Avetisyan, A., & Nesmachnow , S. (2021, Julio). 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. Portland, CT (ID: 27546)
Cortés Mendoza, J. M., Radchenko, G., Chernykh, A., Pulido Gaytan, L. B., Babenko, M., Avetisyan, A., Bouvry, P., & Zomaya, A. (2021, Mayo). LR-GD-RNS: Enhanced Privacy-Preserving Logistic Regression Algorithms for Secure Deployment in Untrusted Environments. 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid). Melbourne, Australia (ID: 27501)
Pulido Gaytan, L. B., Chernykh, A., Cortés Mendoza, J. M., Babenko, M., Radchenko, G., Avetisyan, A., & Drozdov, A. Y. (2021). Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities. Peer-to-Peer Networking and Applications, 14(1), 1666¿1691. doi: 10.1007/s12083-021-01076-8. (ID: 27463)
Chernykh, A., Facio Medina, A., Pulido Gaytan, L. B., Cortés Mendoza, J. M., Radchenko, G., Babenko, M., Chernykh, I., Kulikov, I., Nesmachnow, S., & Rivera Rodríguez, R. (2020, Diciembre). Toward digital twins' workload allocation on clouds with low-cost microservices streaming interaction. 2020 Ivannikov Ispras Open Conference (ISPRAS). Moscu, Rusia (ID: 27552)
Chernykh, A., Babenko, M., Pulido Gaytan, L. B., Golimblevskaia, E., Cortés Mendoza, J. M., & Avetisyan, A. (2020, Diciembre). Experimental Evaluation of Homomorphic Comparison Methods. 2020 Ivannikov Ispras Open Conference (ISPRAS). Moscu, Rusia (ID: 27553)
Babenko, M., Chernykh, A., Golimblevskaia, E., Pulido Gaytan, L. B., & Avetisyan, A. (2020, Noviembre). Homomorphic Comparison Methods: Technologies, Challenges, and Opportunities. En&T-2020 - 2020 International Conference Engineering and Telecommunication. Dolgoprudnyy, Rusia (ID: 27547)
Shiryaev, E., Golimblevskaia, E., Babenko, M., Chernykh, A., & Pulido Gaytan, L. B. (2020, Noviembre). Improvement of the Approximate Method for the Comparison Operation in the RNS. En&T-2020 - 2020 International Conference Engineering and Telecommunication (En&T). Dolgoprudnyy, Rusia (ID: 27548)
Cortés Mendoza, J. M., Chernykh, A., Babenko, M., Pulido Gaytan, L. B., Radchenko, G., Leprevost, F., Wang, X., & Avetisyan, A. (2020, Septiembre). Privacy-Preserving Logistic Regression as a Cloud Service Based on Residue Number System. Russian Supercomputing Days 2020. Moscu, Rusia (ID: 26451)
Pulido Gaytan, L. B., Chernykh, A., Cortés Mendoza, J. M., Babenko, M., & Radchenko, G. (2020, Septiembre). A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption. 2020 Latin American High Performance Computing Conference (CARLA 2020). Cuenca, Ecuador (ID: 27503)
Pulido Gaytan, L. B., Chernykh, A., Nesmachnow , S., Cristobal Salas, A., Avetisyan, A., Castro Barrera, H. E., & Barrios Hernandez, C. B. (2019, Diciembre). Multi-Objective Optimization of Vehicle Routing with Environmental Penalty. International Conference on Supercomputing in Mexico. (ID: 26421)
Pulido Gaytan, L. B., Chernykh, A., Nesmachnow , S., Cristobal Salas, A., Avetisyan, A., Castro, H. E., & Barrios Hernandez, C. B. (2019, Abril). Multi-Objective Optimization of Vehicle Routing with Environmental Penalty. 10mo Congreso Internacional de Supercómputo (ISUM 2019). Monterrey, México (ID: 26477)