Privacy-Preserving Personal Identifiable Information (PII) Label Detection Using Machine Learning

In today's data-driven world, the protection of PII is of paramount importance to safeguard personal privacy. PII tags serve as crucial markers for identifying and processing sensitive information within databases. However, the authentication and registration of PII tags can be time-consuming and error prone. To address this challenge, we propose a method for privacy controlled PII tag detection that harnesses the power of machine learning (ML) combined with regular expressions. Proposed approach leverages various techniques, including feature engineering, adaptive learning, and machine learning, to extract meaningful patterns and relationships from data. By training the model on large datasets that encompass diverse PII elements such as names, addresses, phone numbers, email addresses, and social security numbers, enable it to learn and classify PII identifiers across different documents effectively. One of the key advantages of the proposed method is its ability to automate the detection of PII identifiers, thereby reducing the reliance on manual interpretation and minimizing the potential for human error. By integrating machine learning algorithms, empower organizations to efficiently identify and process sensitive information present in their databases, bolstering privacy protection measures. Moreover, this approach facilitates the development of scalable and accurate solutions for privacy based PII tag search. This advancement paves the way for enhanced data privacy across the enterprise, ensuring compliance with regulations and standards pertaining to the protection of personal information. By combining the strengths of ML and regular expressions, the proposed method enables organizations to detect and handle PII identifiers more effectively. This not only streamlines data management processes but also strengthens privacy safeguards, and more secure and privacy-aware data ecosystem.