Optimizing homomorphic encryption for machine learning operations in cloud computing

This paper will establish how integration of machine learning operation in to cloud computing has greatly enhanced data processing and Analysis. However, data privacy and security has been tricky to achieve as stated earlier. This paper gives a new approach to enhance homomorphic encryption for MLO...

Full description

Saved in:
Bibliographic Details
Published inJournal of information & optimization sciences Vol. 46; no. 4-A; pp. 915 - 925
Main Authors Gowda, V. Dankan, Singh, Shivoham, Kumar, Pullela SVVSR, Dave, Krishna Kant, Kothari, Hemant, Thiruvenkadam, T.
Format Journal Article
LanguageEnglish
Published 2025
Online AccessGet full text
ISSN0252-2667
2169-0103
DOI10.47974/JIOS-1817

Cover

More Information
Summary:This paper will establish how integration of machine learning operation in to cloud computing has greatly enhanced data processing and Analysis. However, data privacy and security has been tricky to achieve as stated earlier. This paper gives a new approach to enhance homomorphic encryption for MLO processes that are carried out in cloud systems. The proposed strategy of solving the problem is effective in restoring computational speed and, as a result, achieving data protection. Based on the results of the experimental assessment it can be stated that the features covered in this paper positively contribute to the reduction of the time required for data processing and the number of sources used with the authenticity of the encrypted information being maintained. Therefore, this work serves the goal of advancing the subject of safe cloud ML by offering an efficient solution for outsourced encryption.
ISSN:0252-2667
2169-0103
DOI:10.47974/JIOS-1817