Overview of homomorphic encryption technology for data privacy

Abstract

This study examines the overview of homomorphic encryption technology for data privacy. In the era of big data, the growing need to utilize vast amounts of information while ensuring privacy and security has become a significant challenge. Homomorphic encryption technology has gained attention as a solution for privacy-preserving data processing, allowing computations on encrypted data without exposing sensitive information. This study introduces the concept of data privacy preservation and explores the evaluation of homomorphic encrypted technology. The focus is on analyzing both partial and full homomorphic encryption methods, highlighting their respective characteristics, evaluation criteria, and the current state of research. Partial homomorphic encryption supports limited operations, while full homomorphic encryption enables unlimited computation on encrypted data, though both face challenges related to computational overhead and efficiency. Additionally, this paper addresses the ongoing issues and limitations associated with homomorphic encryption, such as its complexity, large encryption volumes, and difficulties in handling large-scale datasets. Despite these challenges, researchers continue to refine the technology and expand its applications in cloud computing, big data analytics, and privacy-preserving computing environments. This study also discussed potential future research avenues aimed at improving the scalability, efficiency, and security of homomorphic encryption to support broader, real-world applications. Ultimately, homomorphic encryption is positioned as a key enabler for secure data utilization in an increasingly privacy-conscious digital landscape.