Secure Insights from Data: Algorithms for Privacy-Preserving Mining in the Digital Era
Introduction In the digital age, data mining has become a pivotal tool for extracting valuable insights from vast datasets, driving advancements in business intelligence, healthcare, finance, and social sciences. However, the proliferation of personal data raises profound privacy concerns. Traditional data mining techniques often require access to raw data, which can expose sensitive information such as financial transactions, medical histories, or behavioral patterns. Privacy-preserving data mining (PPDM) addresses this dilemma by developing algorithms that allow knowledge extraction while safeguarding individual privacy. PPDM integrates cryptographic, statistical, and machine learning methods to ensure that insights are derived without revealing underlying personal data. This chapter explores the foundational concepts, key algorithms, practical applications, challenges, and future trends in PPDM. By emphasizing techniques like differential privacy and secure computation, w...