Data Privacy and Data Governance
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.
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Person
Xiaofeng Meng is a Professor at Renmin University of China. He is the Fellow of the China Computer Federation (CCF) and Chinese Association of Artificial Intelligence(CAAI). He has received awards such as the First Prize of the CCF Wang Xuan Award (2009) and the Second Prize of the Beijing Science and Technology Award (2011). He has published over 200 papers in domestic and international journals and conferences and has authored five academic books related to his recent research. He holds 20 national invention patents. His recent research interests include database systems, web and mobile data management, data governance, data intelligence, privacy protection, and social computing.
Content
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.
