A framework for secure cloud operations via homomorphic encryption and euclidean biometric matching

Mittal, Ritik; Kumar, Pradeep; Gaur, Kuntal

doi:10.47974/JDMSC-2650

Open Access Research Article

A framework for secure cloud operations via homomorphic encryption and euclidean biometric matching

Ritik Mittalritik.2410200002@muj.manipal.eduDepartment of Computer Applications Manipal University Jaipur Jaipur, Rajasthan, 303007, IndiaView full profile → , Pradeep Kumarpradeep8984@gmail.comDepartment of Computer Applications Manipal University Jaipur Department of Computer Science & Engineering JSS Academy of Technical EducationNoida, Uttar Pradesh, 201301, IndiaView full profile → , ^*Kuntal GaurCorresponding authorKuntal.gaur@jaipur.manipal.eduDepartment of Computer Applications Manipal University Jaipur Jaipur, Rajasthan, 303007, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 1107–1115Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2650 Crossmark XML

Received:: 07 May 2025
Published Online:: 31 Dec 2025
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2650
Pages:: 1107–1115

Abstract

In the evolving landscape of secure cloud automation, this paper introduces a novel AI-driven framework that integrates homomorphic encryption (HE), facial biometric authentication, and encrypted voice-command processing to achieve intelligent, privacy-preserving cloud resource management. Unlike prior models that depend on plaintext computation or modular scripts, our system performs encrypted biometric matching and command parsing, orchestrating cloud operations through secure tokens generated in the encrypted domain. This architecture, built atop AWS services such as EC2, Lambda, IAM, and Polly, ensures cryptographic isolation and integrity of sensitive workflows. Experimental results demonstrate authentication accuracy of 96.2%, command latency under 2.8 seconds, and significant performance gains over traditional interfaces, with zero leakage under chosen-ciphertext attack simulations. The proposed framework sets a new benchmark for secure, encrypted, and intelligent cloud management.

Keywords

Homomorphic encryption Secure AI Facial biometrics Voice command Cloud automation Cryptographic NLP AWS security Zero-knowledge protocols Privacy preservation

Subject Classifications

Primary 94A60Secondary 68M14

References

[1] C. Gentry, “Fully homomorphic encryption using ideal lattices,” in Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, in STOC ’09. New York, NY, USA: Association for Computing Machinery (2009), pp. 169–178. doi: 10.1145/1536414.1536440.
[2] J. R. Troncoso-Pastoriza, D. Gonzalez-Jimenez, and F. Perez-Gonzalez, “Fully private noninteractive face verification,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 7, pp. 1101–1114 (2013), doi: 10.1109/TIFS.2013.2262273.
[3] M. K. Morampudi, M. V. N. K. Prasad, M. Verma, and U. S. N. Raju, “Secure and verifiable iris authentication system using fully homomorphic encryption,” Computers and Electrical Engineering, vol. 89 (Jan. 2021), doi: 10.1016/j.compeleceng.2020.106924.
[4] G. E. Hinton, L. Deng, D. Yu, G. E. Dahl, A. R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Process Mag, vol. 29, no. 6, pp. 82–97 (2012), doi: 10.1109/MSP.2012.2205597.
[5] A. Nautsch, A. Jimenez, I. Gomez, J. Freire, J. Galbally, and J. Fierrez, “Privacy-Preserving Speaker Recognition with Cohort Score Normalisation,”(Jul. 2019), [Online]. Available: http://arxiv.org/abs/1907.03454
[6] S. Halder and T. Newe, “Enabling secure time-series data sharing via homomorphic encryption in cloud-assisted IIoT,” Future Generation Computer Systems, vol. 133, pp. 351–363 (Aug. 2022), doi: 10.1016/j.future.2022.03.032.
[7] P. Kumar, L. Raja, P. K. Soni, and K. Gaur, “Mathematical morphology based lung segmentation using multiscale dense pyramid network architecture,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 4, pp. 1139–1149 (Jun. 2024), doi: 10.47974/JDMSC-1969.
[8] P. Paillier, “Public-Key Cryptosystems Based on Composite Degree Residuosity Classes,” Springer-Verlag (1999).
[9] I. Chillotti, N. Gama, M. Georgieva, and M. Izabachène, “TFHE: Fast Fully Homomorphic Encryption Over the Torus,” Journal of Cryptology, vol. 33, no. 1, pp. 34–91 (Jan. 2020), doi: 10.1007/s00145-019-09319-x.
[10] T. Ahonen, A. Hadid, and M. Pietikä, “Face Description with Local Binary Patterns: Application to Face Recognition.” [Online]. Available: http://www.ee.oulu.fi/research/imag/texture/lbp/bibliography/
[11] M. P. Morolong, F. B. Shava, and A. M. Gamundani, “Cloud computing security in health cyber physical systems,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 5, pp. 1553–1568 (2023). DOI: 10.47974/JDMSC-1821
[12] S. Rampal, M. Sharma, and P. Khurana, “Crypt analysis of multi-key biometric encryption for business enterprises using cloud storage,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-A, pp. 1559–1568 (2025). DOI: 10.47974/JDMSC-2154

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