TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Security assessment of elliptic curve digital signature variants

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pp. 4623–4636Vol. 29Issue 10-COctober 2026DOI: 10.47974/JDMSC-3078 Crossmark XML
Received:
01 Apr 2026
Published Online:
31 Oct 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-3078
Pages:
4623–4636

Abstract

In the dynamic landscape of securing online communication in the context of blockchain applications, Internet of Things (IoT) systems, and online shopping platforms, where integrity data, authentication, and non-repudiation are crucial, digital signature schemes are necessary. Among these, Elliptic Curve Cryptography has been outstanding due to the ability to provide high level of security with lowest key sizes and computational cost. However, increased sophistication of Internet-based threats and the weaknesses in the implementation-level have raised concerns on the effectiveness of elliptic curve digital signature variants that are widely in use. Present literature is more inclined to discuss these schemes in isolation rather than a holistic scheme of comparing security and performance. The chasm in this paper is that it will offer a comprehensive security analysis of the major elliptic curve signature plans like the Elliptic Curve Digital Signature Algorithm, EdDSA and Schnorr Signature. The study methodology includes mathematical modeling, threat analysis (such as the EUF-CMA, side-channel, and fault attacks) and performance analysis with the measurement of such aspects as the computational efficiency, the security strength, the key size efficiency, and the scalability, and the resources consumption. It has been found out that ECDSA is still widely used, but is more prone to nonce-based attacks and is considerably more expensive to compute. Nevertheless, EdDSA and Schnorr signatures are safest and more effective, and Schnorr has the optimal overall balance, based on the measured metrics.

Keywords

Subject Classifications

68P2594A60

References

[1] A. N. Baracaldo and A. Oprea, “Machine learning security and privacy,” IEEE Secur. Priv., vol. 20, no. 5, pp. 11–13 (2022).

[2] T. Chinbat, S. Madanian, D. Airehrour, and F. Hassandoust, “Machine learning cryptography methods for IoT in healthcare,” BMC Med. Inform. Decis. Mak., vol. 24, Art. no. 153 (2024).

[3] P. Dini, A. Elhanashi, A. Begni, S. Saponara, Q. Zheng, and K. Gasmi, “Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity,” Appl. Sci., vol. 13, no. 13, Art. no. 7507 (2023).

[4] J. Ebrane and S. Lazaar, “An enhanced and verifiable lightweight authentication protocol for securing the Internet of Medical Things (IoMT) based on CP-ABE encryption,” Int. J. Inf. Secur., vol. 23, pp. 3691–3710 (2024).

[5] U. Kashyap, S. K. Padhi, and S. S. Ali, “Attack GAN (AGAN): A new security evaluation tool for perceptual encryption,” arXiv preprint arXiv:2407.06570 (2024).

[6] S. Kumar and D. Sharma, “Key generation in cryptography using elliptic-curve cryptography and genetic algorithm,” Eng. Proc., vol. 59, no. 1, Art. no. 59 (2023).

[7] D. Maimuţ and A. C. Matei, “Speeding-up elliptic curve cryptography algorithms,” Mathematics, vol. 10, no. 19, Art. no. 3676 (2022).

[8] G. Kumar, A. Srivastava, V. S. Sharma, and A. Pillai, “Telemedicine formal verification using lightweight cryptography: An efficient, robust, and secure Radix-16 scalar multiplication in application validate,” J. Discrete Math. Sci. Cryptogr., vol. 28, no. 5-A, pp. 1487–1496 (2025), doi: 10.47974/JDMSC-2147.

[9] P. Singh, P. Pranav, S. Anwar, and S. Dutta, “Leveraging generative adversarial networks for enhanced cryptographic key generation,” Concurr. Comput. Pract. Exp., vol. 36, Art. no. e8226 (2024).

[10] R. C. Poonia, R. Almakki, A. K. J. Saudagar, A. Altameem, and M. Albathan, “A detailed examination of LSTM networks for large-scale text generation with optimizing sequence models,” J. Inf. Optim. Sci., vol. 45, no. 5, pp. 1477–1486 (2024), doi: 10.47974/JIOS-1769.

[11] M. I. Huilgol and S. Asok, “PBIB-designs with constant block sums arising from different graph parameters,” J. Discrete Math. Sci. Cryptogr., vol. 28, no. 7, pp. 2697–2721 (2025), doi: 10.47974/JDMSC-2064.

[12] F. Tellez and J. Ortíz, “Comparing AI algorithms for optimizing elliptic curve cryptography parameters in e-commerce integrations: A pre-quantum analysis,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 11, pp. 1539–1553 (2024).

[13] E. Harris and S. Thompson, “Cyber-physical systems security: Challenges and solutions in IoT devices,” Int. J. Adv. Electr. Comput. Eng., vol. 13, no. 2, pp. 1–6 (2025), doi: 10.65521/ijaece.v13i2.87.

[14] D. J. Bernstein, N. Duif, T. Lange, P. Schwabe, and B.-Y. Yang, “High-speed high-security signatures,” J. Cryptogr. Eng., vol. 2, no. 2, pp. 77–89 (2012).

[15] C. P. Schnorr, “Efficient signature generation by smart cards,” J. Cryptol., vol. 4, no. 3, pp. 161–174 (1991).

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