Toward a Secure Future An Integrated Framework for PII Detection Using NLP and Visual Analysis

This project presents an integrated framework for detecting Personally Identifiable Information (PII) using advanced techniques in Natural Language Processing (NLP) and visual analysis. The primary goal is to develop a system capable of identifying PII from both text and visual data, ensuring privacy and security. The system leverages a combination of deep learning models, including Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Coordinate Attention, Self-Attention, T5Encoder, and Multi-kernel Convolutional Neural Networks (CNN). These models work together to accurately classify and identify sensitive data across various forms of input. The dataset used for this research includes diverse PII examples to train the model effectively. The framework is designed to handle both textual and visual inputs, making it versatile and robust. The system's architecture includes key components such as user registration, prediction, and classification modules, making it user-friendly. The performance of the system is evaluated using standard metrics like accuracy, precision, recall, and F1 score. This project aims to contribute to enhancing data privacy and security practices by enabling automated detection of sensitive information.

Keywords: PII detection, Natural Language Processing, visual analysis, deep learning, GRU, LSTM, Coordinate Attention, Self-Attention, T5Encoder, Multi-kernel CNN.