Securing Sensitive Business Data in Non-Production Environment Using Non-Zero Random Replacement Masking Method

Abstract: The main objective is to protect the privacy of individuals and security of society which is becoming crucial for effective functioning over the business. Privacy enforcement today is being handled primarily through governments legally. Authors aim is to provide uniform data masking architecture for masking the sensitive data like finance retails etc.There is a growing need to protect sensitive employee, customer, and business data across the enterprise wherever such data may reside. Until recently, most data theft occurred from malicious individuals hacking into production databases that were made available across the non-production environment. An important tier of computer data remains practically untouched and unprotected by today’s new data security procedures: nonproduction systems used for in-house development, testing, and training purposes are generally open systems and leave a large hole in the data privacy practices at companies of all sizes. These environments leverage real data to test applications, housing some of the most confidential or sensitive information in an organization, such as Social Security numbers, bank records, and financial documents. This research paper discusses data privacy procedures in nonproduction environments, using a proven commercial solution for masking sensitive data in all nonproduction environments, and integrating these privacy processes and technology across the enterprise. The paper concentrates on data shuffling, substitution, and proposed model Non-Zero Random Replacement masking algorithms. Our results strongly augmented non-zero random replacement method can be used across the domains starting from critical business like finance, banking, and health care. Keywords: Structured data, Un-structured data, Sensitive Data and Data Masking.