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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
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Optimizing homomorphic encryption for machine learning operations in cloud computing
*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication Engineering BMS Institute of Technology and ManagementBangalore, Karnataka, 560119, IndiaView full profile →
, Shivoham Singhshivohamsingh@gmail.comDepartment of Operations Symbiosis Institute of Business Management Symbiosis International (Deemed University)Department of Operations Symbiosis Institute of Business Management Symbiosis International (Deemed to be University)Pune, Hyderabad, 412115, IndiaView full profile →
, Pullela SVVSR Kumarpullelark@yahoo.comDepartment of Computer Science & Engineering Aditya University Surampalem, Andhra Pradesh, 533437, IndiaView full profile →
, Krishna Kant Davekrishnakantdave@gmail.comDepartment of Management Shri Venkateshwara UniversityGajraula, Uttar Pradesh, 244236, IndiaView full profile →
, Hemant Kotharikots.hemant@gmail.comDepartment of PG Studies Pacific Academy of Higher Education & Research UniversityUdaipur, Rajasthan, 313001, IndiaView full profile →
, T. Thiruvenkadammailone.thiru@gmail.comSchool of Information Technology (CODE) SRM UniversitySchool of Computer Science & Information Technology JAIN ( Deemed-to-be University)Bengaluru, Karnataka, 737102, IndiaView full profile →
* Corresponding author · click or hover a name for details
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.
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