Big Data Concept

  Introduction In an era where data is often hailed as the new oil, organizations face mounting pressures to manage it effectively while adhering to stringent regulatory frameworks. Data governance encompasses the policies, processes, and technologies that ensure data is accurate, available, secure, and compliant with legal standards. Compliance, on the other hand, involves aligning these practices with laws such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the US, or sector-specific mandates like HIPAA for healthcare. Enter agentic AI—autonomous systems capable of perceiving their environment, reasoning about tasks, planning actions, and executing them with minimal human intervention. Unlike traditional AI, which is reactive and rule-based, agentic AI operates proactively, adapting to dynamic scenarios through goal-oriented behavior. This chapter explores how agentic AI is revolutionizing data governance and complia...

  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 era of big data, the exponential growth of information generated from various sources has revolutionized industries, particularly healthcare. Electronic health records (EHRs), wearable devices, genomic data, and telemedicine platforms produce vast datasets that enable advanced analytics, personalized medicine, and improved patient outcomes. However, this abundance of data comes with significant privacy risks. Sensitive information, such as medical histories, genetic profiles, and personal identifiers, can be exploited if not adequately protected, leading to identity theft, discrimination, or unauthorized surveillance. Anonymization techniques serve as a cornerstone for safeguarding privacy in big data environments. These methods aim to remove or obscure personally identifiable information (PII) while preserving the utility of the data for analysis. This chapter delves into the principles, methods, and applications of anonymization in large-scale systems, ...