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.

The Consent Management System developed in this project provides a secure and structured platform for managing personal data in compliance with the Digital Personal Data Protection Act (DPDP) 2023. The system ensures that personal data is processed only after obtaining proper consent, with a clear workflow for consent requests, approvals, and withdrawals. It enhances transparency, accountability, and user control over personal data. The platform also supports grievance handling, audit logging, and compliance monitoring to ensure regulatory adherence. By integrating role-based access control and complete consent lifecycle management, the system strengthens data security and prevents unauthorized access. Overall, the project demonstrates an effective approach to building a privacy-focused and compliant data management system.

[1]E. Olca and O. Can, “DICON: A Domain-Independent Consent Management Framework for Personal Data Protection,” IEEE International Conference on Data Security and Privacy, 2022. [2]V. Bhushan, “Empowering Individuals: A Deep Dive into the Digital Personal Data Protection Act, 2023,” Journal of Data Protection and Privacy, 2024. [3]N. Kyriakoulis, “CONSENTIS – An Innovative Framework for Identity and Consent Management for EU Digital and Data Strategies,” International Journal of Information Security, 2025. [4]Y. Wang and Y. Yu, “A Consent-Based Privacy- Compliant Personal Data-Sharing System,” IEEE Access, 2019. [5]W. Neamhom, “Smart Contract-Based Consent Management System for Data Privacy Protection,” Proceedings of Blockchain Technology Conference, 2024. [6]A. S. Jain, “Decoding Consent Managers under the Digital Personal Data Protection Act, 2023: Empowerment Architecture, Business Models and Incentive Alignment,” Data Governance Review, 2025. [7]M. M. Madine et al., “Fully Decentralized Multi-Party Consent Management for Secure Sharing of Patient Health Records,” IEEE Journal of Biomedical and Health Informatics, 2020. [8]Multiple Authors, “Challenges and Solutions in Implementing Informed Consent in Digital Environments: A Scoping Review,” Journal of Digital Ethics and Privacy, 2025. [9]European Union Agency for Cybersecurity (ENISA), “Privacy by Design in Consent Management Systems,” ENISA Technical Report, 2023. National e-Governance Division (NGD), “Business Requirement Document for Consent Management System under the DPDP Act, 2023,” Government of India, 2023.

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