Arlen Prima Dinova
Jurusan Informatika, Fakultas Sains dan Teknologi, UIN Sultan Maulana Hasanuddin Banten

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Performance Evaluation of Homomorphic Encryption Protocols for Cloud Data Processing Lucy Amanda; Arlen Prima Dinova; Amir Faiq Al Hannan; Safira Azahra; Azkal Azkia
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.10303

Abstract

The rapid growth of cloud computing has driven the extensive adoption of cloud-based data storage and processing services across various sectors. Despite its advantages in scalability and efficiency, this paradigm raises significant concerns regarding data security and privacy, particularly when sensitive information is processed by third-party cloud providers. Homomorphic Encryption (HE) has emerged as a promising cryptographic solution to address these challenges, as it enables computations to be performed directly on encrypted data without requiring prior decryption. This study aims to examine the concepts, mechanisms, and protocols of Homomorphic Encryption for secure data processing in cloud environments. The research adopts a literature review method by analyzing various HE schemes, including Partially Homomorphic Encryption, Somewhat Homomorphic Encryption, and Fully Homomorphic Encryption, along with their applications in cloud-based systems. The results indicate that Homomorphic Encryption significantly enhances data confidentiality and privacy during cloud data processing. However, several challenges remain, particularly related to computational complexity and performance efficiency. Nevertheless, Homomorphic Encryption demonstrates strong potential as a foundational technology for developing secure and privacy-preserving cloud services, especially for handling sensitive data such as medical, financial, and personal information.