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Full Description
The book provides a systematic overview of data privacy and data governance from both conceptual and technical perspectives. The first section introduces the fundamentals of data privacy and data governance from a historical and systemic standpoint. Sections two through four focus on large-scale data collection, machine learning, and data ecosystem scenarios, respectively, providing an introduction to their corresponding technical foundations and summarizing key current issues and technological solutions.
This book offers a fresh perspective on data privacy and data governance within the context of the new data ecosystem. It primarily explains technical concepts and algorithms, supplemented by case studies, in a balanced and informative manner. It is intended for both technical professionals seeking information and general readers interested in developing a basic understanding of data privacy and data governance, as well as cultivating data literacy.
The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Contents
Part I Fundamentals Chapter 1: Introduction.- Chapter 2: Data Privacy.- Chapter 3: Data Governance.- Part II Privacy Protection Techniques Chapter 4: Differential Privacy.- Chapter 5: Local Differential Privacy.- Chapter 6: Hybrid Differential Privacy.- Part IIII Privacy in Machine Learning Chapter 7: Privacy Protection in Machine Learning.- Chapter 8: Privacy Protection in Federated Learning.- Chapter 9: Machine Unlearning.- Part IV Data Ecosystem and Data Governance Chapter 10: Data Market.- Chapter 11: Data Monology.- Chapter 12: Data Fairness.- Chapter 13: Data Transparency.
