A Proposed Approach for the Key Generation in Cryptography to Enrich the Data Confidentiality While Sharing Data over the Network

https://doi.org/10.55529/jaimlnn.24.26.32

Authors

  • Eeva N. Kapopara Ph.D. Scholar Department of Computer science and technology Sardar Patel University, Vallabh Vidyanagar, Gujarat, India
  • Dr. Prashant P. Pittalia Associate Professor Department of Computer science and technology Sardar Patel University, Vallabh Vidyanagar, Gujarat, India

Keywords:

Cryptography, Symmetric Key, Asymmetric Key, Public Key, Private Key, Encryption, Decryption

Abstract

Data sharing over network has been threat in the digital world. To provide the security to the data being passed over the network various system has been defined. Most widely used systems are cryptography and steganography. Cryptography is the art of converting the data to some another format which will not be understandable by the intruders. The most important phase while sharing data over network using cryptography is the key generation and key distribution. General cryptography techniques either uses public key or uses public and private key both. The major focus of the propose method is not to commute keys with the data being shared over network. Proposed approach defines a method which will get feedback from the receiver and system will identify whether the receiver is undeniable or not. If the receiver is undeniable then only the data will be decrypted to the original to the original format. In this paper we have surveyed many traditional algorithms related to symmetric key and asymmetric key cryptography. Many mathematical operations are being applied to generate the complex keys to ensure security. This approach defines the complexity with almost no mathematical complexity and using no traditional approach for the key generation which are widely known.

Published

2022-07-10

How to Cite

Eeva N. Kapopara, & Dr. Prashant P. Pittalia. (2022). A Proposed Approach for the Key Generation in Cryptography to Enrich the Data Confidentiality While Sharing Data over the Network. Journal of Artificial Intelligence,Machine Learning and Neural Network (JAIMLNN) ISSN: 2799-1172, 2(04), 26–32. https://doi.org/10.55529/jaimlnn.24.26.32