Big Data And Differential Privacy
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Big Data
Author: Mr. Rohit Manglik
language: en
Publisher: EduGorilla Publication
Release Date: 2024-07-29
EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels.
Web and Big Data
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed conference proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30–September 1, 2024. The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions. The papers are organized in the following topical sections: Volume I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System. Volume II: Recommender System, Knowledge Graph and Spatial and Temporal Data. Volume III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization. Volume IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security Volume V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
Data Privacy and Big Data
Author: Aadinath Pothuvaal
language: en
Publisher: Educohack Press
Release Date: 2025-01-03
The illustrations in this book are created by “Team Educohack”. Data Privacy and Big Data: A Foundational Guide is your essential resource for understanding the importance of data privacy and its critical role in data security. As firms expand and data volumes grow, safeguarding data becomes increasingly vital. This book offers comprehensive knowledge on the subject, ensuring data is handled correctly and protected from misuse. We begin with an introduction to data privacy, followed by chapters on machine learning, statistical learning, and the implications of data protection. The book also covers compliance tools, classification approaches for Big Data security, and the evolving landscape of user privacy and innovation. A crucial chapter on big data privacy explores privacy models, disclosure risk measures, and data masking methods. We also delve into performance measurement of big data analytics and selecting appropriate data masking techniques. The book concludes with a compelling case study on data forensics, providing practical insights. Data Privacy and Big Data: A Foundational Guide is an indispensable guide for anyone looking to navigate the complexities of data privacy in today’s digital world.