Introduction.- Machine and Statistical Learning.- On the Classification of Protection Procedures.- User’s privacy.- Privacy Models and Disclosure Risk Measures.- Masking methods.- Information loss: evaluation and measures.- Selection of masking methods.- Conclusions.

​This book offers a broad, cohesive overview of the field of data privacy. It discusses, from a technological perspective, the problems and solutions of the three main communities working on data privacy: statistical disclosure control (those with a statistical background), privacy-preserving data mining (those working with data bases and data mining), and privacy-enhancing technologies (those involved in communications and security) communities.Presenting different approaches, the book describes alternative privacy models and disclosure risk measures as well as data protection procedures for respondent, holder and user privacy. It also discusses specific data privacy problems and solutions for readers who need to deal with big data.

Includes recent research on data privacy Provides background information as well as new developments Discusses the challenge that big data presents for data privacy Includes supplementary material: sn.pub/extras Includes supplementary material: sn.pub/extras