Secure Data Masking Through Synthetic Data Generation using ML

As the need for privacy and data security, organizations face the challenge of using sensitive data for development, testing, and analytics while ensuring compliance with data protection laws. This paper introduces a novel approach to secure data masking by leveraging Synthetic Data Generation through machine learning, particularly Generative Adversarial Networks (GANs). Instead of traditional masking or anonymization techniques for sensitive information such as Personally Identifiable Information (PII), this approach generates synthetic yet realistic data that retains the statistical properties and structure of the original dataset. The generated synthetic data ensures privacy compliance by eliminating real sensitive information while preserving essential attributes for effective analysis, testing, and development. This project addresses the need for robust synthetic data generation frameworks that can be seamlessly integrated into existing data pipelines, providing both scalability and security.