Lattice-based homomorphic encryption for privacy-preserving medical analytics
Mayuri H. Molawademhmolawade@bvucoep.edu.inDepartment of Computer EngineeringCollege of EngineeringBharati Vidyapeeth (Deemed to be University)Pune, Maharashtra, 411043, IndiaView full profile → , *Prabhat SharmaCorresponding authorprabhat.sharma.orp@chitkara.edu.inCentre for Research Impact and OutcomeChitkara UniversityRajpura, Punjab, 140417, IndiaView full profile → , Kukatla Tejeshkukatla.tejesh.kt@gmail.comCentre for Multidisciplinary ResearchAnurag UniversityHyderabad, Telangana, 500088, India0009-0003-3330-1918View full profile → , Samaksh Goyalsamaksha.law@quantumeducation.inQuantum University Research CenterQuantum UniversityRoorkee, Uttarakhand, 247667, India0009-0008-1367-9530View full profile → , Vipulkumar M. Dabhidean.tech@gokuluniversity.ac.inFaculty of EngineeringGokul Global UniversitySidhpur, Gujarat, 384151, IndiaView full profile → , Yazdani Hasanyazdani.hasan@niu.edu.inDepartment of Computer SciencesNoida International UniversityGreater Noida, Uttar Pradesh, 203201, 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-3017
- Pages:
- 3887–3898
Abstract
The medical analytics that protect privacy has gained urgency as the amount of sensitive healthcare information has been increasing rapidly and regulatory restrictions are imposed. The present paper presents a lattice-based homomorphic encryption system that allows performing computations on encrypted medical data without revealing patient data. The research to be conducted utilizes the Learning with Errors (LWE), Ring-LWE and Module-LWE assumptions, which guarantee post-quantum security and are still computationally feasible. It has efficient polynomial arithmetic, batching algorithms and novel noise management schemes to improve performance. Experimental analysis shows that the suggested solution can lead to an increase in up to 34.8% of the computation efficiency and a decrease in latency by 41.2% as compared to homomorphic schemes in the baseline scenarios and maintain data confidentiality. The framework assists in real-time analytics diagnostics, predictive, and clinical decision models.
Keywords
Subject Classifications
References
[1] H. Zhang, M. Shang, H. Liu, and D. Zhang, “Lattice-based multi-key homomorphic encryption scheme without common random strings,” Symmetry, vol. 17, no. 5, Art. no. 722 (2025), doi: 10.3390/sym17050722.
[2] W. Yang, S. Wang, H. Cui, Z. Tang, and Y. Li, “A review of homomorphic encryption for privacy-preserving biometrics,” Sensors, vol. 23, no. 7, Art. no. 3566 (2023), doi: 10.3390/s23073566.
[3] F. Haq, C. Chen, and Z. Chen, “Privacy-preserving classification of medical tabular data with homomorphic encryption,” Algorithms, vol. 18, no. 12, Art. no. 731 (2025), doi: 10.3390/a18120731.
[4] H. Jorge, C. Wanzeller, and J. Henriques, “Evaluating homomorphic encryption schemes for privacy and security in healthcare data management,” Journal of Cybersecurity and Privacy, vol. 5, no. 3, Art. no. 74 (2025), doi: 10.3390/jcp5030074.
[5] I. Walskaar, M. C. Tran, and F. O. Catak, “A practical implementation of medical privacy-preserving federated learning using multi-key homomorphic encryption and Flower framework,” Cryptography, vol. 7, no. 4, Art. no. 48 (2023), doi: 10.3390/cryptography7040048.
[6] S. M. Stefanov, “Numerical solution of box constrained separable convex quadratic programming problems,” Journal of Information and Optimization Sciences, vol. 45, no. 1, pp. 57–71 (2024), doi: 10.47974/JIOS-1267.
[7] M. M. Salim, I. Kim, U. Doniyor, C. Lee, and J. H. Park, “Homomorphic encryption based privacy-preservation for IoMT,” Applied Sciences, vol. 11, no. 18, Art. no. 8757 (2021), doi: 10.3390/app11188757.
[8] W. Xu, J. Sun, R. Cardell-Oliver, A. Mian, and J. B. Hong, “A privacy-preserving framework using homomorphic encryption for smart metering systems,” Sensors, vol. 23, no. 10, Art. no. 4746 (2023), doi: 10.3390/s23104746.
[9] J. Park and H. Lim, “Privacy-preserving federated learning using homomorphic encryption,” Applied Sciences, vol. 12, no. 2, Art. no. 734 (2022), doi: 10.3390/app12020734.
[10] C. M. Anish, A. Jadhav, H. N. Datir, G. S. Mate, A. Ashirova, and A. Rawat, “Lightweight AI-supported ECC algorithms for security in IoT and edge devices,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 29, no. 2-B, pp. 779–787 (2026), doi: 10.47974/JDMSC-2524.
[11] Y. Kurt Peker and R. Raj, “Homomorphic encryption for confidential statistical computation: Feasibility and challenges,” Journal of Cybersecurity and Privacy, vol. 6, no. 1, Art. no. 4 (2026), doi: 10.3390/jcp6010004.
[12] A Majeed, S. Patni, and S. O. Hwang, “A comprehensive analysis of privacy-preserving solutions developed for IoT-based systems and applications,” Electronics, vol. 14, no. 11, Art. no. 2106 (2025), doi: 10.3390/electronics14112106.
[13] 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.
[14] L. Attapong, “Recent advances in dynamic path-controllable deep unfolding network to predict the K-barriers for intrusion detection using a wireless sensor network: A systematic review,” International Journal of Recent Advances in Engineering and Technology, vol. 14, no. 2, pp. 96–103 (Nov. 2025).
[15] D. Syed, S. S. Refaat, and O. Bouhali, “Privacy preservation of data-driven models in smart grids using homomorphic encryption,” Information, vol. 11, no. 7, Art. no. 357 (2020), doi: 10.3390/info11070357.
