A Novel Approach to Data Encryption Based on Matrix Computations Rosilah Hassan 1 , Selver Pepic 2 , Muzafer Saracevic 3 , Khaleel Ahmad 4,* and Milan Tasic 5 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: khaleelahmad@manuu.edu.in Received: 26 July 2020; Accepted: 22 August 2020 Abstract: In this paper, we provide a new approach to data encryption using gen- eralized inverses. Encryption is based on the implementation of weighted Moore– Penrose inverse A y MN nxm ð Þ 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 com- pared by achieved results of classification concentrating on classes. In a compara- tive analysis, we give results of classifying of advanced encryption standard (AES) algorithm and proposed encryption method based on Moore–Penrose inverse. Keywords: Security; data encryption; matrix computations; cloud computing; machine learning 1 Introduction The level of security of data stored on the cloud is primarily based on the identification of sensitive and confidential databases, and it is necessary to apply additional protection, encryption, and monitoring. It is important to consider whether it is possible to encrypt data at all levels, where they are designed, and how encryption algorithms are tested. Data encryption became of great importance in many fields including healthcare [1], and several encryption methods have been investigated including triple data encryption [2]. The most basic way cloud providers provide data is encryption. Indeed, using clouds in This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computers, Materials & Continua DOI:10.32604/cmc.2020.013104 Article ech T Press Science