Big Data Concept

  Introduction In the realm of big data, where organizations amass terabytes of information from diverse sources, collaborative analytics holds immense promise for unlocking collective insights. Industries like healthcare, finance, and supply chain management benefit from pooled data to enhance decision-making, predict trends, and innovate. However, sharing raw data poses severe risks, including privacy breaches, intellectual property theft, and regulatory non-compliance. Secure Multi-Party Computation (SMPC) emerges as a cryptographic paradigm that allows multiple parties to jointly compute functions on their private inputs without revealing the inputs themselves—only the output is disclosed. SMPC, rooted in cryptographic research from the 1980s, has evolved to address big data's scale, enabling distributed computations across clouds, edge devices, and federated systems. This chapter explores SMPC's principles, protocols, applications in big data analytics, challenges, an...

  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...