Open Access
OPTIMIZING CRYPTOGRAPHIC HASH FUNCTION PERFORMANCE THROUGH AN EXTENDED SECURE HASH ALGORITHM (2080-BIT VARIANT)
4
Centre For Information Security Research, Indian Statistical Institute, Kolkata, India
4
School Of Computer And Systems Sciences, Jawaharlal Nehru University, New Delhi, India
Abstract
Cryptographic hash functions are fundamental to ensuring data integrity, authentication, and security in digital systems. This paper introduces and evaluates an extended 2080-bit variant of the Secure Hash Algorithm, designed to enhance resistance against collision, preimage, and length-extension attacks while maintaining computational efficiency. The proposed algorithm incorporates dynamic message expansion, multi-stage compression, and parallel processing techniques to optimize performance across diverse hardware architectures. Benchmarking results reveal that the 2080-bit variant outperforms conventional SHA families in both throughput and cryptographic strength, making it suitable for high-security applications such as blockchain, digital forensics, and secure communications. This study advances the development of robust, scalable hash functions for future-proof security systems.
Keywords
Cryptographic hash function,
secure hash algorithm,
SHA-2080,
data integrity
References
Ambedkar, B. R., Bharti, P. K., & Husain, A. (2022). Enhancing the Performance of Hash Function Using Autonomous Initial Value Proposed Secure Hash Algorithm 256. 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT).
Ambedkar, B. R., Bharti, P. K., & Husain, A. (2021). Design and Analysis of Hash Algorithm Using Autonomous Initial Value Proposed Secure Hash Algorithm64. 2021 IEEE 18th India Council International Conference (INDICON).
Mathew, S., & Jacob, K. P. (2010). Performance Evaluation of Popular Hash Functions. World Academy of Science, Engineering and Technology, pp.449–452.
Zheng, X., Hu, X., Zhang, J., Yang, J., Cai, S., & Xiong, X. (2019). An Efficient and Low-Power Design of the SM3 Hash Algorithm for IoT. Electronics, 8(9), 9. DOI: 10.3390/electronics8091033.
Yavuz, A. A., & Ozmen, M. O. (2019). Ultra Lightweight Multiple-time Digital Signature for the Internet of Things Devices. IEEE Transactions on Services Computing, pp.1–1. DOI: 10.1109/TSC.2019.2928303.
Santini, P., Baldi, M., & Chiaraluce, F. (2019). Cryptanalysis of a One-Time Code-Based Digital Signature Scheme. 2019 IEEE International Symposium on Information Theory (ISIT), pp.2594–2598. DOI: 10.1109/ISIT.2019.8849244.
Mohammed Ali, A., & Kadhim Farhan, A. (2020). A Novel Improvement With an Effective Expansion to Enhance the MD5 Hash Function for Verification of a Secure E-Document. IEEE Access, 8, 80290–80304. DOI: 10.1109/ACCESS.2020.2989050.
Samiullah, M., et al. (2020). An Image Encryption Scheme Based on DNA Computing and Multiple Chaotic Systems. IEEE Access, 8, 25650–25663. DOI: 10.1109/ACCESS.2020.2970981.
Singh, L., Singh, A. K., & Singh, P. K. (2020). Secure data hiding techniques: a survey. Multimed Tools Appl, 79(23), 15901–15921. DOI: 10.1007/s11042-018-6407-5.
De Guzman, F. E., Gerardo, B. D., & Medina, R. P. (2019). Implementation of Enhanced Secure Hash Algorithm Towards a Secured Web Portal. 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), pp.189–192. DOI: 10.1109/CCOMS.2019.8821763.
Faz Hernández, A., Fujii, H., Aranha, D., & López, J. (2017). A Secure and Efficient Implementation of the Quotient Digital Signature Algorithm (qDSA). pp.189. DOI: 10.1007/978-3-319-71501-8_10.
Fei, X., Li, K., Yang, W., & Li, K. (2016). A secure and efficient file protecting system based on SHA3 and parallel AES. Parallel Computing, 52, 106–132. DOI: 10.1016/j.parco.2016.01.001.
Madhuravani, B., & Murthy, D. S. R. (2013). Cryptographic hash functions: SHA family. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2(4), 326–329.
Ideguchi, K., Owada, T., & Yoshida, H. (2009). A Study on RAM Requirements of Various SHA-3 Candidates on Low-cost 8-bit CPUs. 260.
Melnyk, V. A., & Kit, A. Y. (2013). Basic Operations of Modern Hashing Algorithms. COMPUTER SCIENCE, p. 4.
Liang, W., Xie, S., Long, J., Li, K.-C., Zhang, D., & Li, K. (2019). A double PUF-based RFID identity authentication protocol in service-centric internet of things environments. Information Sciences, 503, 129–147. DOI: 10.1016/j.ins.2019.06.047.
Zhu, S., Zhu, C., & Wang, W. (2018). A new image encryption algorithm based on chaos and secure hash SHA-256. Entropy, 20(9), 716.
Grassi, L., Khovratovich, D., Rechberger, C., Roy, A., & Schofnegger, M. (2021). Poseidon: A New Hash Function for Zero-Knowledge Proof Systems. Presented at the 30th {USENIX} Security Symposium ({USENIX} Security 21), pp. 519–535.
Kuznetsov, A., Lutsenko, M., Kuznetsova, K., Martyniuk, O., Babenko, V., & Perevozova, I. (2020). Statistical Testing of Blockchain Hash Algorithms. p. 13.
Similar Articles
- Dr. Evelyn R. Chen, Dr. Adrian M. Vella, A Comprehensive Taxonomy and Critical Survey of Scientific Workflow Scheduling Paradigms in IaaS Cloud Computing: Evaluating Fitness for High-Stakes Environmental Modeling , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 11 (2025): Volume 02 Issue 11
- Elena M. Kovacs, Predictive Intelligence Across Physical and Financial Systems: A Comparative Research Framework for Packed-Bed Thermal Energy Storage and AI-Driven Forecasting , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Claire Whitman, LEVERAGING CYBER THREAT INTELLIGENCE MINING FOR ENHANCED PROACTIVE CYBERSECURITY: A COMPREHENSIVE REVIEW AND FUTURE DIRECTIONS , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Dr. Laura Stein, ADVANCING PROACTIVE CYBERSECURITY THROUGH CYBER THREAT INTELLIGENCE MINING: A COMPREHENSIVE REVIEW AND FUTURE DIRECTIONS , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 02 (2025): Volume 02 Issue 02
- Dr. Dmitry V. Sokolov, Synergizing Generative AI and Explainable Machine Learning in Security Operations Centers: Mitigating Alert Fatigue and Enhancing Analyst Performance , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- John M. Callahan, Advancing Cyber Threat Intelligence Frameworks: Integrative Models, Sharing Mechanisms, and Predictive Analytics , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 07 (2025): Volume 02 Issue 07
- Dr. Mateo Alvarez-Ruiz, From Reactive to Predictive Security: Integrating Threat Intelligence with SIEM for Proactive Threat Hunting , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Julian R. Cortez, A Comparative Analysis of Image Encryption Techniques Based on Linear Feedback Shift Registers and Chaotic Systems , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- Dr. Elena Petrova, Dr. Hassan Al-Mansoori, EVALUATING AND ENHANCING CYBERSECURITY AND RESILIENCE IN HEALTHCARE: A UNIFIED RISK AND COMPLIANCE FRAMEWORK , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 05 (2025): Volume 02 Issue 05
- Dr. Layla Hassan, Reem Al-Mazrouei, EVOLVING PARADIGMS AND FUTURE TRAJECTORIES IN CYBER THREAT INTELLIGENCE , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 06 (2025): Volume 02 Issue 06
You may also start an advanced similarity search for this article.