A Robust Dynamic Data Masking Transformation approach To Safeguard Sensitive Data

Abstract

Large amount of digital data is generated rapidly all around the corners. Providing security to digital data is the crucial issue in almost all types of organizations. According to the Identity Theft Resource Center, there were 8,069 data breaches between January 2005 and November 2017, and in recent years the number of data breaches and compromised records has skyrocketed [1]. To provide protection to the digital sensitive data, from data breaches in the need of hour. Almost all domains like insurance, banking, health care, and educational and many more are concern about security of sensitive data. Data masking is one of the vital discussions everywhere as data breach leads to threats. Masking is a philosophy or new way of thinking about safeguarding sensitive data in such a way that accessible and usable data is still available for non- production environment. In this research paper authors proposed a dynamic data masking model to protect sensitive data using random deterministic masking algorithm with shift left approach. This paper describes methodology & experimental design and results.