Effective Optimization strategies to utilize Fully Homomorphic Encryption in Cloud Platforms

Seethur Raveendra, Prajwal (2024) Effective Optimization strategies to utilize Fully Homomorphic Encryption in Cloud Platforms. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the age of big data, meeting the data regulatory requirements is a fundamental need for companies who store and process private data in the cloud. The GDPR states that any personal information processed by any business has to be done with consent and with high security. Privacy-preserving models can be implemented in cloud environment to store and process personal data. These models guarantee privacy protection of each individual’s private information but implanting these models can be computationally intensive. Homomorphic Encryption is privacy-preserving model that performs arithmetic operations on encrypted data without decryption. The first Fully Homomorphic Encryption scheme was invented in 2009 but it was not fit for real world applications because of its high computational requirements. Previous research focused on algorithmic advancements while this research aims to provide an efficient approach to utilize the existing Fully Homomorphic Encryption algorithms(schemes). A framework was developed using data splitting, parallel processing and AWS Lambda. The tenseal python library was used to homomorphically encrypt the values of a synthetic health care dataset. The evaluation was performed and results are reported. The results show a drastic 94% CPU time reduction when compared to other FHE schemes and a significant 82.88% reduction in memory usage after using data splitting and parallel processing.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kazmi, Aqeel UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	04 Jul 2025 10:42
Last Modified:	04 Jul 2025 10:42
URI:	https://norma.ncirl.ie/id/eprint/8052

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