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Data Science in Practice pp 121–132Cite as

Data Privacy

Part of the Studies in Big Data book series (SBD,volume 46)

Abstract

In this chapter we present an overview of the topic data privacy. We review privacy models and measures of disclosure risk. These models and measures provide computational definitions of what privacy means, and of how to evaluate the privacy level of a data set. Then, we give a summary of data protection mechanisms. We provide a classification of these methods according to three dimensions: whose privacy is being sought, the computations to be done, and the number of data sources. Finally, we describe masking methods. Such methods are the data protection mechanisms used for databases when the data use is undefined and the protected database is required to be useful for several data uses. We also provide a definition of information loss (or data utility) for this type of data protection mechanism. The chapter finishes with a summary.

Keywords

  • Data Protection Mechanisms
  • Disclosure Risk
  • Masking Method
  • Privacy Model
  • Differential Privacy

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 7.1

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Author information

Authors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Vicenç Torra & Klara Stokes

  2. Universitat Autònoma de Barcelona, Campus UAB, Bellaterra, Catalonia, Spain

    Guillermo Navarro-Arribas

Authors
  1. Vicenç Torra
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  2. Guillermo Navarro-Arribas
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  3. Klara Stokes
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Corresponding author

Correspondence to Vicenç Torra .

Editor information

Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Alan Said

  2. University of Skövde, Skövde, Sweden

    Prof. Vicenç Torra

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Torra, V., Navarro-Arribas, G., Stokes, K. (2019). Data Privacy. In: Said, A., Torra, V. (eds) Data Science in Practice. Studies in Big Data, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-97556-6_7

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