SECURE DATA STORAGE IN CLOUD COMPUTING USING QAES AND NTRU ALGORITHMS

Abstract

Information must be kept private and secure because it is shared when people communicate online, making data protection essential. Data sharing is a significant element of this. Information can now be viewed by unauthorised in terceptors due to the volume of data being transmitted online. Cryptography has been a key component of security systems. A blank message is encrypted during this process to add protection. An exact copy ensures sufficient protection of data in both conventional and quantum computing is urgently needed given the rise of quantum computing, as encryption is currently the most popular method of cloud data protection. Symmetric key cryptosystems, in comparison to public key cryp tosystems, are faster because they only need a single private key to encode and decrypt data at both ends. Even so, it can be challenging to maintain security in a hostile environment while carrying out compatible and effective key distribu tion and secure private data transmission across organisations. In this paper com prehensive analysis of this cryptosystem is presented and describes the compo nent-by-component approach used in its implementation. The different attacks on the McEliece cryptosystem are covered separately. The experimental results obtained using Goppa codes are also reported in the research where the simula tions are carried out at different extension degrees. Using the results of the sim ulations, we came to our findings about the outcomes and the numerous imple mentation issues. In this project, a model is proposed that applies the Cloud cus tomer data security using NTRUs (nth degree truncated polynomial ring units) together with a McEliece variation cryptosystem to secure access control data. To encrypt and decode data, the modified NTRU cryptosystem is employed., which is powered by lattice math. The process of multiplying larger numbers or conducting complex multiplication is known as lattice multiplication, which divides the process into smaller steps while maintaining an algorithm that is pre cisely the same as the traditional long multiplication method