Advancing Network Security with Artificial Intelligence: A Study on Anomaly Detection in Traffic Data

Privacy-preserving data science in healthcare has become increasingly important as the industry shifts towards data-driven decision-making. Privacy refers to the protection of sensitive patient information from unauthorized access, misuse, or disclosure. Maintaining confidentiality while allowing meaningful analysis poses a significant challenge as the healthcare data volume and complexity keep rising. Traditional privacy protection methods such as encryption, anonymization, and access control have been widely used but often fail to maintain data utility or prevent advanced re-identification attacks. Emerging privacy-preserving techniques such as federated learning, homomorphic encryption, differential privacy, and secure multi-party computation provide innovative solutions by enabling collaborative model training and analysis without compromising individual data privacy. These models are important in the healthcare domain, where decentralized data and strict privacy regulations limit data sharing. The objective of this study is to systematically review and analyze these privacy-preserving techniques in healthcare, identify their strengths and limitations, and suggest directions for future improvement. The findings indicate that while these methods enhance confidentiality and enable distributed analytics, they still face challenges related to computational overhead, interoperability, and scalability. This literature-based study explores the development of privacy-preserving data science methods, discusses the limitations of existing techniques, and highlights the need for robust, secure, and ethical data science frameworks in healthcare.