SECURITY AND STORAGE ENHANCEMENT OF CLOUD ENTERPRISE RESOURCE PLANNING DATA USING HOMOMORPHIC ENCRYPTION AND SECRET SHARING

Abstract

In this thesis, a number of solutions are proposed to enhancing and improving the security and confidentiality of Cloud Enterprise Resource Planning (ERP) Data. Firstly, the Asmuth-Bloom, Blakley, Mignote and other Secret Sharing Schemes (SSS) are reviewed, adopted and modified in order to present a relatively improved secret sharing scheme. Conditions for the scheme is also presented as well as algorithms for implementation of the scheme presented by this research. Secondly, a hybrid of two homomorphic encryption scheme is presented to address chosen ciphertext attacks (CCA) on Cloud ERP Data. The Rivest-Shamir-Adleman (RSA) and Paillier cryptosystems are adopted and modified to present an improved double-layer encryption homomorphically. A System architecture for Video Conferencing in the midst of the pandemic COVID-19 and beyond is presented as well as algorithms for the implementation of same. The hybrid of two homomorphic encryption schemes presented in this thesis do not share keys with the cloud. Thirdly, this thesis presents Homomorphic encryption scheme using the Redundant Residue Number System (RRNS), Geometric Probability, Bernoulli Probability and the concept of secret sharing schemes (SSS). Parameters are deducted and presented based on the reports from Kaspersky lab. The effectiveness of the scheme presented in this research work is demonstrated in the ability to handle data redundancy as well as error detection and correction. Finally, a comprehensive load balancing scheme is presented to handle load management of the Cloud ERP Data shares in a multi-cloud environment. The Weighted Round Robin (WRR) scheme is modified. A dynamic weight (Wd) is introduced to share the Cloud ERP Data shares in the multi-cloud environment. The dynamic weight (Wd) is calculated using the data variance, the root mean square to generate the dynamic coefficient. The load balancing scheme presented solves data loss and delay concerns in load balancing. All the proposed schemes are meant to enhance the security, efficiency and computational integrity of Cloud ERP data homomorphically. The performance of the proposed schemes are evaluated theoretically and simulated using python and compared with other schemes. The comparison analysis suggests the proposed schemes presented in this thesis work offer a substantial improvement over the other schemes.