Advancing homomorphic encryption techniques for ensuring privacy in cloud-based analytics

Khidse, Swati V.; Sohi, Davinder Kaur; Bibhu, Vimal; Nandgaonkar, Vikas; Singla, Atul; Swarnkar, Suman Kumar

doi:10.47974/JDMSC-2505

Open Access Research Article

Advancing homomorphic encryption techniques for ensuring privacy in cloud-based analytics

Swati V. Khidseswatikhidse@gmail.comDepartment of Computer Science and EngineeringCSMSS Chh. Shahu College of EngineeringAurangabad, Maharashtra, 431002, IndiaView full profile → , Davinder Kaur Sohidavinder.kaur@symlaw.ac.inSymbiosis Law SchoolSymbiosis Centre for Advanced Legal Studies and Research (SCALSAR), Symbiosis International (Deemed University)Pune, Maharashtra, 412115, IndiaView full profile → , ^*Vimal BibhuCorresponding authorvimal.bhibu@niu.edu.inDepartment of School of Engineering & TechnologyNoida International UniversityNoida, Uttar Pradesh, 203201, IndiaView full profile → , Vikas Nandgaonkarvikas.nandgaonkar@indiraicem.ac.inDepartment of Computer EngineeringIndira College of Engineering and ManagementPune, Maharashtra, 410506, IndiaView full profile → , Atul Singlaatul.singla@lpu.co.inSchool of Architecture and DesignLovely Professional UniversityPhagwara, Punjab, 144411, IndiaView full profile → , Suman Kumar Swarnkarsumanswarnkar17@gmail.comDepartment of Computer Science and EngineeringShri Shankaracharya Institute of Professional Management and TechnologyRaipur, Chhattisgarh, 492015, IndiaView full profile →

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pp. 629–636Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2505 Crossmark XML

Received:: 09 Apr 2025
Published Online:: 04 Feb 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2505
Pages:: 629–636

Abstract

Vulnerabilities in modern cloud analytics have become more pronounced with the rapid adoption of cloud-based analytics in high impact and sensitive domains like healthcare, finance and IoT; the demand for privacy preserving means that scale to these large-scale problems has intensified. However, existing traditional encryption schemes do not allow processing calculation on encrypted data, enabling the use of Homomorphic Encryption (HE) in analytics workflow. In this paper we propose Hybrid Homomorphic Encryption with Differential Privacy Calibration (HHE-DPC), a novel hybrid approach, that fuses the computational flexibility of leveled HE with the statistical guarantees of differential privacy. The proposed framework provides end-to-end confidentiality with respect to data processing over cloud systems and keeps analytical utility. The effectiveness of HHE DPC is validated with existing privacy preserving methods in terms of accuracy, communication overhead, computational cost, throughput, and convergence. Experimental results show that HHE-DPC exhibits better performance: the accuracy is improved by up to 5.4% and computation overhead is reduced up to 30% compared with competition schemes, including state of the art PPFL-LQDP.

Keywords

Homomorphic encryption Cloud-based analytics Privacy preservation Differential privacy Secure computation

Subject Classifications

Primary 93A30Secondary 49K15

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