Data Privacy: Foundations, New Developments and the Big Data Challenge

verfasst von: Vicenç Torra

Verlag: Springer International Publishing

Buchreihe : Studies in Big Data

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Large amounts of data are collected and processed nowadays. Data privacy is to ensure that disclosure of sensitive information does not take place. In this chapter we give an introduction to the field. We describe the motivations for data privacy, underline the links between data privacy and the society, and review terminology and concepts.

Vicenç Torra

Chapter 2. Machine and Statistical Learning

Abstract

Databases and big data are used for constructing models to have a better understanding of the data, or to make decisions. Machine and statistical learning offer tools for this purpose. In this chapter we review some of the methods in these areas that are of relevance in this book.

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Chapter 3. On the Classification of Protection Procedures

Abstract

The literature on data privacy offers a large number of methods. In this chapter we present a classification of these methods according to three different dimensions. The chapter also discusses result-driven approaches and methods for tabular data.

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Chapter 4. User’s Privacy

Abstract

We have users’ privacy when users have an active role to protect their own privacy. In this chapter we review user privacy in communications and in information retrieval.

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Chapter 5. Privacy Models and Disclosure Risk Measures

Abstract

A fundamental issue in order to define effective methods for ensuring confidentiality is to define privacy models as well as measures for disclosure risk assessment. In this chapter we review the main models and measures.

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Chapter 6. Masking Methods

Abstract

This chapter describes major methods for protecting databases. This includes perturbative and non-perturbative methods, as well as synthetic data generators. The chapter also includes a discussion on methods for big data.

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Chapter 7. Information Loss: Evaluation and Measures

Abstract

Masking methods modify databases in order to avoid disclosure. This causes some information loss that can be quantified. In this chapter we discuss different alternatives to evaluate in what extent relevant information is lost. We give an overview of generic and specific information loss measures.

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Chapter 8. Selection of Masking Methods

Abstract

A good masking method is the one that avoids disclosure with low information loss, or that achieves a good trade-off between disclosure risk and information loss. In this chapter we describe tools to help in the selection of a masking method.

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Chapter 9. Conclusions

Abstract

This chapter summarizes the content of the book and provides a final discussion on different privacy models and data privacy mechanisms.

Vicenç Torra

Backmatter

Titel: Data Privacy: Foundations, New Developments and the Big Data Challenge
verfasst von: Vicenç Torra
Verlag: Springer International Publishing
Electronic ISBN: 978-3-319-57358-8
Print ISBN: 978-3-319-57356-4
DOI: https://doi.org/10.1007/978-3-319-57358-8