Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Knowledge discovery from data has become an essential process in domains like healthcare, finance, education, and smart cities. However, the increasing volume of personal and sensitive information in datasets raises serious privacy concerns. Privacy-preserving knowledge discovery (PPKD) aims to extract useful patterns and models without exposing confidential information of individuals or organizations. This paper reviews the foundations, techniques, and applications of privacy-preserving knowledge discovery. It discusses major approaches such as anonymization, cryptographic computation, differential privacy, federated learning, and secure multi-party mining. A comparative analysis is presented to evaluate trade-offs between data utility and privacy guarantees. The paper also highlights challenges like scalability, regulatory compliance, adversarial attacks, and interpretability. Emerging research directions including privacy-aware deep learning and decentralized data mining are also explored. The study shows that achieving strong privacy with high analytical accuracy remains a complex but critical goal for trustworthy data-driven systems.

Keywords: Privacy-preserving data mining, knowledge discovery, differential privacy, secure multi-party computation, federated learning, data anonymization