Algebraic cryptographic primitives for secure IoT data aggregation
Yogesh Dinkar Jadhavsittpo3@gmail.comDepartment of Mechanical EngineeringSinhgad College of EngineeringSavitribai Phule Pune UniversityPune, Maharashtra, 411041, IndiaView full profile → , Jyotsna Suryavanshijyotsna.suryavanshi@vit.eduDepartment of Engineering Science and HumanitiesVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , *Padmavati ShrivastavaCorresponding authorshrivastava.padmavati@gmail.comDepartment of Computer Science and Engineering (AI & ML)Rungta International Skills UniversityBhilai, Chhattisgarh, 490024, IndiaView full profile → , Pawan Arunkumar Upadhyepawan.arunkumar@gmail.comDepartment of Electronics and Telecommunication EngineeringSymbiosis Institute of Technology (SIT)Pune, Maharashtra, 412115, IndiaView full profile → , Megha Jaggamegha.jagga.orp@chitkara.edu.inCentre of Research Impact and OutcomeChitkara UniversityRajpura, Punjab, 140401, IndiaView full profile → , K. Parimalaparim@maher.ac.inDepartment of PharmacologyMeenakshi Medical College Hospital and Research InstituteMeenakshi Academy of Higher Education & Research (Deemed to be University)Kanchipuram, Tamil Nadu, 631552, 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-3020
- Pages:
- 3925–3936
Abstract
The rapid growth of Internet of things (IoT) networks requires the effective and secure data aggregation systems that would maintain the confidentiality and provide real-time analytics. The present paper introduces an algebraic cryptographic approach to the problem of secure IoT data aggregation, basing it on structured mathematical primitives to guarantee computation on distributed sensor data upholding privacy. The framework combines algebraic encoding, homomorphic transformation and lightweight encryption schemes to facilitate aggregation in a secure manner without revealing raw data. A formalized mathematical model is built that models the aggregation operations in modular algebraic spaces which are computationally efficient and adversarial resilient. The recommended algorithms are measured in terms of the level of security, the accuracy of aggregation, and the rate of communication showing enhancements in efficiency and lower latency than traditional algorithms. Findings show improved scalability, resiliency and applicability in resource limited IoT settings, which make the framework viable in actual deployments.
Keywords
Subject Classifications
References
[1] A. Carelli, A. Palmieri, A. Vilei, F. Castanier, and A. Vesco, “Enabling secure data exchange through the IOTA Tangle for IoT constrained devices,” Sensors, vol. 22, no. 4, Art. no. 1384 (2022), doi: 10.3390/s22041384.
[2] W. Fang, C. Zhu, and A. W. H. Ip, “Advances in security, trust and privacy in Internet of Things,” Applied Sciences, vol. 16, no. 6, Art. no. 3086 (2026), doi: 10.3390/app16063086.
[3] S. Cirani, G. Ferrari, and L. Veltri, “Enforcing security mechanisms in the IP-based Internet of Things: An algorithmic overview,” Algorithms, vol. 6, no. 2, pp. 197–226 (2013), doi: 10.3390/a6020197.
[4] K. Limniotis, “Cryptography as the means to protect fundamental human rights,” Cryptography, vol. 5, no. 4, Art. no. 34 (2021), doi: 10.3390/cryptography5040034.
[5] J. J. M. Rabuni, R. M. Devi, S. Subhalakshmi, and J. Sudha, “Analysis of operation on spherical fuzzy soft topology and its application,” Journal of Interdisciplinary Mathematics, vol. 28, no. 7, pp. 2707–2732 (2025), doi: 10.47974/JIM-2329.
[6] A. K. Sharma, N. Purohit, S. Joshi, I. U. Lakkewar, and P. Khobragade, “Securing IoT environments: Deep learning-based intrusion detection,” in Deep Learning for Intrusion Detection, F. S. Masoodi and A. Bamhdi, Eds. Wiley (2026), doi: 10.1002/9781394285198.ch12.
[7] K. Tsantikidou and N. Sklavos, “Hardware limitations of lightweight cryptographic designs for IoT in healthcare,” Cryptography, vol. 6, no. 3, Art. no. 45 (2022), doi: 10.3390/cryptography6030045.
[8] R. Longo, C. Mascia, A. Meneghetti, G. Santilli, and G. Tognolini, “Adaptable cryptographic primitives in blockchains via smart contracts,” Cryptography, vol. 6, no. 3, Art. no. 32 (2022), doi: 10.3390/cryptography6030032.
[9] Z. Dewamuni, B. Shanmugam, S. Azam, and S. Thennadil, “Bibliometric analysis of IoT lightweight cryptography,” Information, vol. 14, no. 12, Art. no. 635 (2023), doi: 10.3390/info14120635.
[10] S. Shamshad, F. Riaz, R. Riaz, S. S. Rizvi, and S. Abdulla, “An enhanced architecture to resolve public-key cryptographic issues in the Internet of Things (IoT), employing quantum computing supremacy,” Sensors, vol. 22, no. 21, Art. no. 8151 (2022), doi: 10.3390/s22218151.
[11] D. H. F. Ansari and R. Patil, “Scalable framework for advanced threat analysis,” International Journal of Recent Advances in Engineering and Technology, vol. 14, no. 3s, pp. 147–151 (2025), doi: 10.65521/intjournalrecadvengtech.v14i3s.1683.
[12] R. C. Poonia, R. Almakki, A. K. J. Saudagar, A. Altameem, and M. Albathan, “An optimization approach for real-time object detection in IoT devices through edge computing and deep learning,” Journal of Information and Optimization Sciences, vol. 45, no. 5, pp. 1465–1475 (2024), doi: 10.47974/JIOS-1768.
[13] L. Leonardi, G. Lettieri, P. Perazzo, and S. Saponara, “On the hardware–software integration in cryptographic accelerators for industrial IoT,” Applied Sciences, vol. 12, no. 19, Art. no. 9948 (2022), doi: 10.3390/app12199948.
