Sensitive Data Classification Using Deep Network with Sequential Models: An Analysis of Performance and Comparative Study

With the growing volume of sensitive data, there is a significant need for efficient and accurate methods for data classification. Traditional classification methods are often limited in their ability to handle large-scale datasets with high-dimensional features. On the other hand, deep learning approaches have demonstrated promising results in a variety of classification problems because of their capacity to acquire intricate representations from data. Using Convolutional Neural Networks (CNNs) to automatically extract characteristics from raw data and then feeding them into a fully connected layer for classification, a deep learning-based strategy is used for classifying sensitive data in this research. We test the suggested strategy on a sensitive data-filled real-world dataset and compare its performance to numerous cutting-edge classification techniques. Experimental findings show that our method achieves excellent accuracy and outperforms conventional classification techniques in sensitive data classification tasks. Our proposed method provides a practical and effective solution for automated sensitive data classification, which can help organizations protect their sensitive information from unauthorized access or disclosure.