Open Access

ARTICLE

A Novel Approach to Data Encryption Based on Matrix Computations

by Rosilah Hassan¹, Selver Pepic², Muzafer Saracevic³, Khaleel Ahmad^4,*, Milan Tasic⁵

1 Centre for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia
2 Technical Machine School of Professional Studies, Radoja Krstića 19, Trstenik, 37240, Serbia
3 University of Novi Pazar, Dimitrija Tucovića bb, Novi Pazar, 36300, Serbia
4 Maulana Azad National Urdu University, Hyderabad, Telangana, 500032, India
5 University of Nis, Višegradska 33, Niš, 18106, Serbia

* Corresponding Author: Khaleel Ahmad. Email:

(This article belongs to the Special Issue: Deep Learning Trends in Intelligent Systems)

Computers, Materials & Continua 2021, 66(2), 1139-1153. https://doi.org/10.32604/cmc.2020.013104

Received 26 July 2020; Accepted 22 August 2020; Issue published 26 November 2020

Abstract

In this paper, we provide a new approach to data encryption using generalized inverses. Encryption is based on the implementation of weighted Moore–Penrose inverse A^†_MN(nxm) over the nx8 constant matrix. The square Hermitian positive definite matrix N_8x8 p is the key. The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge. We have provided NIST (National Institute of Standards and Technology) quality assurance tests for a random generated Hermitian matrix (a total of 10 different tests and additional analysis with approximate entropy and random digression). In the additional testing of the quality of the random matrix generated, we can conclude that the results of our analysis satisfy the defined strict requirements. This proposed MP encryption method can be applied effectively in the encryption and decryption of images in multi-party communications. In the experimental part of this paper, we give a comparison of encryption methods between machine learning methods. Machine learning algorithms could be compared by achieved results of classification concentrating on classes. In a comparative analysis, we give results of classifying of advanced encryption standard (AES) algorithm and proposed encryption method based on Moore–Penrose inverse.

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Citations