Classification Task-Oriented Method of Differentially Private Data Publishing With Fine-Grained Correlations Preservation and Class Labels Preservation

This paper presents a new method for publishing data while protecting privacy using differential privacy (a technique that adds noise to data to hide individual information). The method is designed specifically for classification tasks (training AI models to categorize data), and it improves performance by keeping class labels (the correct categories for data records) unchanged and preserving correlations (relationships between different data attributes) using large language models and clustering algorithms.