A Data Masking Utility to Secure Sensitive Production Data in Non-Production Environment
Ruby Jain, Manimala Puri, Umesh Jain · International Journal of Knowledge Engineering and Data Mining · 2019
The right to privacy is a fundamental right. However, despite increasing recognition and awareness of data protection, there is still a lack of legal and institutional frameworks, processes, and infrastructure support to protect data and privacy rights. The increasing use of personal data, with the emergence of technologies, enabling new ways of processing and using it, means that regulating an effective data protection framework is more important than ever. Researchers developed a non-deterministic masking technique that provides security to sensitive data, without modifying the appearance of original values of confidential variables. In this study, we develop such a new procedure and discuss its underlying theory. Referred to as non-deterministic masking, this procedure will allow organisations to publish and share data for non-production environment, with minimal disclosure risk. The proposed model is evaluated on a real financial dataset and the results showed masked dataset as similar as the original dataset.