Secure Employee Data Hiding in Relational Databases Using the Elliptic Curve Diffie-Hellman Approach.

Abstract

Data masking is greatly efficient in securing data openings and cannot allow hijackers to easily hack the system. An effective approach aims to minimize data privacy breaches and involves developing techniques that leverage encryption for efficient data security. Nevertheless, it is important to hide data in a way that preserves its actual identity for authorized personnel while masking it from cybercriminals to prevent breaches. The process of data hiding is indicated to obtain a clear layout of the dynamic masking process and acquire an efficient solution for the security of a database. This research aims to propose a cryptographic approach Elliptic Curve Diffie Hellman (ECDH) for securely hiding the employee data in relational databases which provide a strong confidentiality level. Initially, the employee data is collected and stored by an organization regarding its employees and a distributed database is performed on different sites that cannot share physical components. The Secure Hash Algorithm (SHA-256) is utilized to hash a encrypted data. After obtaining encrypted data, data hiding is performed to efficiently embed the extended secret data. Then, data parsing is established to convert data from one format to another for structuring data. Finally, the secret data extraction and retrieval are performed on the receiver side.