This paper explores the intersection of machine learning and personal data privacy, examining the challenges and solutions for preserving privacy in data-driven systems. As machine learning algorithms increasingly rely on large datasets, concerns about data leakage and breaches have intensified. To address these issues, we investigate various privacy-preserving techniques, including differential privacy, federated learning, adversarial training, and data anonymization. The findings highlight the effectiveness of these methods in protecting sensitive information while maintaining model performance. However, trade-offs in accuracy, computational efficiency, and model interpretability remain significant challenges. The paper also emphasizes the need for transparent and explainable models to ensure ethical data use and foster trust in AI systems. Ultimately, the study concludes that while privacy-preserving machine learning methods show great promise, ongoing research is essential to balance privacy and performance in future applications.

Machine learning, data privacy, differential privacy

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