Big Data Concept

  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, ...

  Introduction Big data has transformed industries by enabling unprecedented insights, predictive capabilities, and operational efficiencies. However, its real-world implementation introduces significant challenges in security, privacy, and ethics. The scale, variety, and velocity of big data amplify risks related to unauthorized access, data breaches, and ethical misuse. This chapter explores these challenges and provides practical strategies for responsible implementation, emphasizing safeguards, regulatory compliance, and ethical considerations. By addressing these issues, organizations can harness big data's potential while mitigating risks and fostering trust. 4.1 Big Data Security: Risks and Safeguards Understanding Security Risks Big data environments are prime targets for cyberattacks due to the volume and value of data they store. Key risks include: Data Breaches : Unauthorized access to sensitive datasets, such as personal or financial information, can lead to signifi...