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Privacy in Data Publishing

  • Sabrina De Capitani di Vimercati
  • Sara Foresti
  • Giovanni Livraga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6514)

Abstract

In modern digital society, personal information about individuals can be easily collected, shared, and disseminated. These data collections often contain sensitive information, which should not be released in association with respondents’ identities. Removing explicit identifiers before data release does not offer any guarantee of anonymity, since de-identified datasets usually contain information that can be exploited for linking the released data with publicly available collections that include respondents’ identities. To overcome these problems, new proposals have been developed to guarantee privacy in data release. In this chapter, we analyze the risk of disclosure caused by public or semi-public microdata release and we illustrate the main approaches focusing on protection against unintended disclosure. We conclude with a discussion on some open issues that need further investigation.

Keywords

Equivalence Class External Knowledge Sensitive Attribute Disclosure Risk Protection Technique 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sabrina De Capitani di Vimercati
    • 1
  • Sara Foresti
    • 1
  • Giovanni Livraga
    • 1
  1. 1.Università degli Studi di MilanoCremaItalia

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