The rapid growth of digital systems has led organizations to store large volumes of sensitive information, including Personally Identifiable Information. Improper handling of such data can result in privacy breaches and significant security risks. This paper presents a Data Privacy Enforcement Tool that focuses on protecting sensitive data using data masking techniques. The proposed system identifies PII fields within datasets and applies masking methods such as partial masking, character replacement and format- preserving masking to conceal confidential information while retaining its usability. The system is implemented using a Django-based backend and a React-based web interface, allowing users to upload datasets, configure masking rules and export the protected data in formats such as CSV, JSON and Excel. By restricting the solution to data masking, the tool ensures simplicity, efficiency and ease of integration into existing data workflows. Experimental results demonstrate that the proposed approach effectively protects sensitive information while maintaining the structural integrity and usability of the data for analysis and testing purposes.

This work presents a data masking–based privacy enforcement framework for securing PII in structured datasets. The system integrates rule-based PII detection with configurable masking algorithms to transform sensitive attributes while preserving schema consistency and data utility. Techniques such as partial masking, character replacement, substitution, and format-preserving masking are systematically applied to ensure that original values are irreversibly concealed without affecting downstream usability. The proposed architecture, built using a Django backend and React interface, enables efficient data ingestion, rule configuration, and transformation workflows within a unified environment. The masking process is designed to maintain referential integrity and structural constraints, allowing the masked datasets to be seamlessly utilized in development, testing, and analytical pipelines. Experimental evaluation indicates that the system achieves effective privacy protection with minimal impact on data usability. By emphasizing transformation-based security over traditional access control mechanisms, the proposed approach demonstrates a scalable and practical solution for privacy preservation. Overall, this work highlights data masking as a robust technique for enforcing data privacy in modern data management systems.

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