Lana A. Gagloeva
Research Subjects
Research Interests
Lana A. Gagloeva has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Andrei Tchernykh
CICESE Research Center, Ensenada, Mexico
1 shared publication - Arutyun Avetisyan
Ivannikov Institute for System Programming of the Russian Academy of Sciences
1 shared publication - Bernardo Pulido-Gaytan
Affiliation Computer Science Department, CICESE Research Center, Ensenada, BC, Mexico
1 shared publication - Egor Shiriaev
North-Caucasus Federal University
1 shared publication - Elena Golimblevskaia
Computer Science Department, University of Potsdam, 14469 Potsdam, Germany
1 shared publication Georgii Valuev
North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 355017 Stavropol, Russia
1 shared publication
Latest Publications
- Open Access
- 28 April 2023
- Published by MDPI AG in Big Data and Cognitive Computing
- Vol. 7 (2) , 84
A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption
Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices.
- 28 April 2023
- Published by MDPI AG in Big Data and Cognitive Computing
- Vol. 7 (2) , 84
Open AccessA Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption
Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices.