DIFFERENTIAL PRIVACY PROTECTION TECHNOLOGY REVIEW FOR MACHINE LEARNING

Abstract

Machine Learning (ML) has become a fundamental technology for intelligent decision-making across domains such as healthcare, finance, cybersecurity, transportation, education, and smart cities. Despite its remarkable predictive capabilities, ML systems rely heavily on large-scale datasets containing sensitive personal and organizational information, making them vulnerable to privacy breaches and inference attacks. Conventional privacy-preserving techniques, including encryption, anonymization, and access control, provide limited protection because they cannot effectively prevent membership inference, model inversion, or data reconstruction attacks. To address these challenges, Differential Privacy (DP) has emerged as a mathematically rigorous privacypreserving framework that ensures the confidentiality of individual records while maintaining the analytical utility of machine learning models. This paper presents a comprehensive Differential Privacy Protection Framework for Machine Learning that integrates privacy-preserving mechanisms into the complete ML pipeline. The proposed framework supports dataset acquisition, preprocessing, machine learning model training, differential privacy implementation, privacy budget analysis, utility evaluation, visualization, and automated report generation. Logistic Regression, Random Forest, and Differentially Private Logistic Regression are employed to evaluate the impact of privacy budgets on classification performance. Performance assessment is conducted using accuracy, precision, recall, F1-score, and privacy-utility trade-off metrics under varying epsilon values. The framework enables researchers to systematically analyze how different privacy configurations influence predictive performance while ensuring formal privacy guarantees. Interactive visualization modules provide insights into privacy consumption and model effectiveness, allowing users to identify optimal privacy settings for different applications. Experimental analysis demonstrates that carefully selected privacy budgets can significantly reduce privacy leakage while preserving acceptable prediction accuracy, thereby achieving an effective balance between privacy and utility. The proposed system offers a scalable, transparent, and user-friendly environment for privacyaware machine learning research and deployment. It supports regulatory compliance, enhances user trust, and contributes to the development of secure, ethical, and explainable artificial intelligence systems capable of protecting sensitive information without substantially compromising machine learning performance.