A Biometric Asymmetric Cryptosystem Software Module Based on Convolutional Neural Networks

Full Text (PDF, 208KB), PP.1-12

Views: 0 Downloads: 0


Ilyenko Anna 1,* Ilyenko Sergii 1 Herasymenko Marharyta 1

1. National aviation university, Kyiv 03058, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2021.06.01

Received: 13 Jun. 2021 / Revised: 11 Aug. 2021 / Accepted: 21 Oct. 2021 / Published: 8 Dec. 2021

Index Terms

Convolutional neural network, Biometric cryptographic systems, Biometric features, Secret key, Authentication


During the research, the analysis of the existing biometric cryptographic systems was carried out. Some methods that help to generate biometric features were considered and compared with a cryptographic key. For comparing compact vectors of biometric images and cryptographic keys, the following methods are analyzed:  designing and training of bidirectional associative memory; designing and training of single-layer and multilayer neural networks. As a result of comparative analysis of algorithms for extracting primary biometric features and comparing the generated image to a private key within the proposed authentication system, it was found that deep convolutional networks and neural network bidirectional associative memory are the most effective approach to process the data. In the research, an approach based on the integration of a biometric system and a cryptographic module was proposed, which allows using of a generated secret cryptographic key based on a biometric sample as the output of a neural network. The RSA algorithm is chosen to generate a private cryptographic key by use of convolutional neural networks and  Python libraries. The software authentication module is implemented based on the client-server architecture using various internal Python libraries. Such authentication system should be used in systems where the user data and his valuable information resources are stored or where the user can perform certain valuable operations for which a cryptographic key is required. Proposed software module based on convolutional neural networks will be a perfect tool for ensuring the confidentiality of information and for all information-communication systems, because protecting information system from unauthorized access is one of the most pressing problems. This approach as software module solves the problem of secure generating and storing the secret key and author propose combination of the convolutional neural network with bidirectional associative memory, which is used to recognize the biometric sample, generate the image, and match it with a cryptographic key. The use of this software approach allows today to reduce the probability of errors of the first and second kind in authentication system and absolute number of errors was minimized by an average of 1,5 times. The proportion of correctly recognized images by the comparating together convolutional networks and neural network bidirectional associative memory in the authentication software module increased to 96,97%, which is on average from 1,08 times up to 1,01 times The authors further plan a number of scientific and technical solutions to develop and implement effective methods, tools to meet the requirements, principles and approaches to cybersecurity and cryptosystems for provide integrity and confidentiality of information in experimental computer systems and networks.

Cite This Paper

Ilyenko Anna, Ilyenko Sergii, Herasymenko Marharyta, "A Biometric Asymmetric Cryptosystem Software Module Based on Convolutional Neural Networks", International Journal of Computer Network and Information Security(IJCNIS), Vol.13, No.6, pp.1-12, 2021. DOI: 10.5815/ijcnis.2021.06.01


[1] Uludag, U., Pankanti, S., Prabhakar, S., & Jain, A. K. (2004). Biometric cryptosystems: issues and challenges. Proceedings of the IEEE, 92(6), 948-960.

[2] Jain, A. K., Nandakumar, K., & Nagar, A. (2008). Biometric template security. EURASIP Journal on advances in signal processing, 2008, 1-17.

[3] Jain, A. K., Ross, A., & Uludag, U. (2005, September). Biometric template security: Challenges and solutions. In 2005 13th European signal processing conference (pp. 1-4). IEEE.

[4] Biometrics: definition, use cases and latest news. [Online]. – Available: https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/inspired/biometrics

[5] Maček, N., Franc, I., Gnjatović, M., Trenkić, B., Bogdanoski, M., & Aleksić, A. Biometric Cryptosystems–Approaches to Biometric Key-Binding and Key-Generation. Univerzitet Metropolitan Beograd 20. oktobar 2018. godine, 16.

[6] Juels, A., & Wattenberg, M. (1999, November). A fuzzy commitment scheme. In Proceedings of the 6th ACM conference on Computer and communications security (pp. 28-36).

[7] Bodo, A. (1994). Method for producing a digital signature with aid of a biometric feature. German patent DE, 42(43), 908.

[8] Nandakumar, K., Jain, A. K., & Pankanti, S. (2007). Fingerprint-based fuzzy vault: Implementation and performance. IEEE transactions on information forensics and security, 2(4), 744-757.

[9] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377.

[10] Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1-15.

[11] Minhas, R. A., Javed, A., Irtaza, A., Mahmood, M. T., & Joo, Y. B. (2019). Shot classification of field sports videos using AlexNet Convolutional Neural Network. Applied Sciences, 9(3), 483.

[12] Ding, S., Li, H., Su, C., Yu, J., & Jin, F. (2013). Evolutionary artificial neural networks: a review. Artificial Intelligence Review, 39(3), 251-260.

[13] Dodis, Y., Katz, J., Reyzin, L., & Smith, A. (2006, August). Robust fuzzy extractors and authenticated key agreement from close secrets. In Annual International Cryptology Conference (pp. 232-250). Springer, Berlin, Heidelberg.

[14] Boyen, X., Dodis, Y., Katz, J., Ostrovsky, R., & Smith, A. (2005, May). Secure remote authentication using biometric data. In annual international conference on the theory and applications of cryptographic techniques (pp. 147-163). Springer, Berlin, Heidelberg.

[15] Sahai, A., & Waters, B. (2005, May). Fuzzy identity-based encryption. In Annual international conference on the theory and applications of cryptographic techniques (pp. 457-473). Springer, Berlin, Heidelberg.

[16] Baek, J., Susilo, W., & Zhou, J. (2007, March). New constructions of fuzzy identity-based encryption. In Proceedings of the 2nd ACM symposium on Information, computer and communications security (pp. 368-370).

[17] Vihar Kurama, Samhita Alla, Rohith Vishnu K, " Image Semantic Segmentation Using Deep Learning", International Journal of Image, Graphics and Signal Processing, Vol.10, No.12, pp. 1-10, 2018.

[18] Priya Gupta, Nidhi Saxena, Meetika Sharma, Jagriti Tripathi,"Deep Neural Network for Human Face Recognition", International Journal of Engineering and Manufacturing, Vol.8, No.1, pp.63-71, 2018.

[19] Kalid A.Smadi, Takialddin Al Smadi,"Automatic System Recognition of License Plates using Neural Networks", International Journal of Engineering and Manufacturing, Vol.7, No.4, pp.26-35, 2017.

[20] Safiia Mohammed, Michael Hegarty,"Evaluation of Voice & Ear Biometrics Authentication System", International Journal of Education and Management Engineering, Vol.7, No.4, pp.29-40, 2017.

[21] Vanaja Roselin.E.Chirchi, Laxman.M.Waghmare,"Iris Biometric Authentication used for Security Systems", International Journal of Image, Graphics and Signal Processing, vol.6, no.9, pp.54-60, 2014.

[22] Jyoti Malik,Dhiraj Girdhar,Ratna Dahiya,G. Sainarayanan, "Reference Threshold Calculation for Biometric Authentication", International Journal of Image, Graphics and Signal Processing, vol.6, no.2, pp.46-53, 2014.

[23] K.Usha, M.Ezhilarasan,"A Hybrid Model for Biometric Authentication using Finger Back Knuckle Surface based on Angular Geometric Analysis", International Journal of Image, Graphics and Signal Processing, vol.5, no.10, pp.45-54, 2013.

[24] Anouar Ben Khalifa,Najoua Essoukri BenAmara,"Contribution to the Fusion of Biometric Modalities by the Choquet Integral", International Journal of Image, Graphics and Signal Processing, vol.4, no.10, pp.1-7, 2012.

[25] Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9(4), 611-629.

[26] Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2414-2423).

[27] Sylvester, J. J. (1889). On the reduction of a bilinear quantic of the nth order to the form of a sum of n products by a double orthogonal substitution. Messenger of Mathematics, 19(6), 42-46.

[28] Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.

[29] The convolutional neural network. [Online]. – Available: https://intellect.ml/svertochnaya-nejronnaya-set-convolutional-neural-network-cnn-6013

[30] The Complete Beginner’s Guide to Deep Learning: Convolutional Neural Networks and Image Classification. [Online]. – Available: https://towardsdatascience.com/wtf-is-image-classification-8e78a8235acb

[31] Convolutional Neural Networks as a modern approach in AI. [Online]. – Available: https://svitla.com/blog/convolutional-neural-networks-as-a-modern-approach-in-ai

[32] Convolutional_neural_network. [Online]. – Available: https://en.wikipedia.org/wiki/Convolutional_neural_network

[33] Kosko, B. (1988). Bidirectional associative memories. IEEE Transactions on Systems, man, and Cybernetics, 18(1), 49-60.

[34] Feng, C., & Plamondon, R. (2001). On the stability analysis of delayed neural networks systems. Neural networks, 14(9), 1181-1188.

[35] Kazmirchuk, S., Anna, I., & Sergii, I. (2019, January). Digital signature authentication scheme with message recovery based on the use of elliptic curves. In International Conference on Computer Science, Engineering and Education Applications (pp. 279-288). Springer, Cham.

[36] Ilyenko, A., Ilyenko, S., & Prokopenko, O. (2020). The improvement of ntruencrypt public key cryptosystem:design and performance evaluation. Cybersecurity: education, science, technique, 2(10), 123-134. https://doi.org/10.28925/2663-4023.2020.10.123134

[37] Kazmirchuk, S., Ilyenko, A., Ilyenko, S., Olesya, Y., Herasymenko, M., & Iavich, M. (2020). Improved Gentry’s Fully Homomorphic Encryption Scheme: Design, Implementation and Performance Evaluation.

[38] Kazmirchuk, S., Ilyenko, A., Ilyenko, S., Prokopenko, O., & Mazur, Y. (2020, January). The Improvement of Digital Signature Algorithm Based on Elliptic Curve Cryptography. In International Conference on Computer Science, Engineering and Education Applications (pp. 327-337). Springer, Cham.