Discrete algebraic methods in secure distributed systems
Vinod V. Kimbahunevinod.kimbahune@vit.eduDepartment of Computer EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , *Chudaman Devidasrao SukteCorresponding authorrajeshsukte@gmail.comDepartment of Information TechnologyVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Neeta Deshpandeneeta.deshpande@ges-coengg.orgDepartment of Computer EngineeringGokhale Education society'sR H Sapat College of Engineering, Management Studies and ResearchNashik, Maharashtra, 412115, IndiaView full profile → , Umesh Rautumesh.raut@mitwpu.edu.inDepartment of Computer Engineering and TechnologyDr. Vishwanath Karad MIT World Peace UniversityPune, Maharashtra, 411038, IndiaView full profile → , Suhas Chavanchavan.suhas18@gmail.comDepartment of Computer EngineeringVishwakarma UniversityKondhwa, Pune, Maharashtra, 411048, IndiaView full profile → , Aarti S. Gaikwadaratig.2010@gmail.comDepartment of Information TechnologyD. Y. Patil College of EngineeringAkurdi, Pune, Maharashtra, 411044, IndiaView full profile → , Chandrakant Deelip Kokanecdkokane1992@gmail.comDepartment of Computer Science and Engineering (Artificial Intelligence)Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3053
- Pages:
- 4309–4320
Abstract
The use of distributed systems in various fields, which has grown up, including cloud computing, IoT, and decentralized networks, has compounded the necessity to have an effective and robust security mechanism. Although traditional cryptographic designs are good, they can be characterized by large computation overhead and poor scalability in dynamic distributed system. In this work, we tackle these issues by coming up with a discrete algebraic security model, which makes use of finite field arithmetic and encoding with the help of polynomials to perform secure distributed computation. The main goal is to come up with a scalable and effective model that will provide data confidentiality, integrity, and antagonism with adversarial attacks. The key generation methodology combines algebra-based and encryption key generation techniques, and distributed computation of encrypted data, which is backed by a layered system architecture. The metrics that are used to evaluate performance include encryption/decryption time, throughput, latency, security strength and fault tolerance. The resulting experimentation proves the suggested framework to decrease the computational latency by about 25-40% and enhance the throughput up to 1.8 times compared to traditional RSA/ECC algorithms and be highly resistant to attacks. These results suggest that the suggested solution is a reasonable trade-off between security and efficiency, thus it is extremely applicable to distributed systems in the real world.
Keywords
Subject Classifications
References
[1] R. G. D’Oliveira, S. E. Rouayheb, and D. Karpuk, “GASP codes for secure distributed matrix multiplication,” IEEE Trans. Inf. Theory, vol. 66, no. 7, pp. 4038–4050 (2020).
[2] R. G. D’Oliveira, S. E. Rouayheb, D. Heinlein, and D. Karpuk, “Degree tables for secure distributed matrix multiplication,” IEEE J. Sel. Areas Inf. Theory, vol. 2, no. 3, pp. 907–918 (2021).
[3] H. H. López, G. L. Matthews, and D. Valvo, “Secure MatDot codes: A secure, distributed matrix multiplication scheme,” in Proc. IEEE Inf. Theory Workshop (ITW), Mumbai, India, pp. 149–154 (Nov. 2022).
[4] E. Byrne, O. W. Gnilke, and J. Kliewer, “Straggler-and adversary-tolerant secure distributed matrix multiplication using polynomial codes,” Entropy, vol. 25, no. 2, p. 266 (2023).
[5] S. M. Stefanov, “Multi-objective optimization revisited,” Journal of Information and Optimization Sciences, vol. 47, no. 3, pp. 833–848 (2026), doi: 10.47974/JIOS-1395.
[6] R. C. Poonia, T. Singh, C. K. Rajashri, D. Mondal, and S. M. Patil, “Optimizing communication system security through comprehensive testing and intelligent network configurations,” Journal of Information and Optimization Sciences, vol. 47, no. 5-A, pp. 1709–1717 (2026), doi: 10.47974/JIOS-2261.
[7] R. Dathathri, B. Kostova, O. Saarikivi, W. Dai, K. Laine, and M. Musuvathi, “EVA: An encrypted vector arithmetic language and compiler for efficient homomorphic computation,” in Proc. 41st ACM SIGPLAN Conf. Programming Language Design and Implementation (PLDI), pp. 546–561 (2020).
[8] Y. Ishai, E. Kushilevitz, R. Ostrovsky, and A. Sahai, “Zero-knowledge from secure multiparty computation,” in Proc. 39th Annu. ACM Symp. Theory of Computing (STOC), New York, USA, pp. 21–30 (2007).
[9] V. G. Shrimant, R. Halam, G. Tiwari, S. Chilukuri, R. Ramesh, and A. Singla, “Developing secure communication protocols using elliptic curve cryptography for IoT devices,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 29, no. 2-A, pp. 533–541 (2026), doi: 10.47974/JDMSC-2493.
[10] D. Boneh, B. Lynn, and H. Shacham, “Privacy-enhancing cryptographic techniques in modern applications,” ACM Comput. Surveys, vol. 54, no. 2, pp. 1–35 (2021).




