Privacy Preservation in Big Data Analytics provides a comprehensive introduction to the concepts, techniques, and challenges of protecting sensitive information in the era of big data. Covering topics such as data anonymization, encryption, differential privacy, secure data sharing, privacy-preserving machine learning, cloud security, IoT, and AI-driven privacy solutions, the book combines theoretical foundations with practical applications and real-world case studies. Designed for students, researchers, and professionals, it offers valuable insights into building secure, ethical, and privacy-aware data analytics systems while addressing emerging trends and regulatory requirements in today's data-driven world.