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Privacy Preserving and Data Mining in an On-Line Statistical Database of Additive Type

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Privacy in Statistical Databases (PSD 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3050))

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Abstract

In an on-line statistical database, the query-answering system should prevent answers to statistical queries from leading to disclosure of confidential data. On the other hand, a statistical user is inclined to data mining, that is, to disclose pieces of information that are implicit in the (explicit) answers to his queries. A key task for both is to find data that is derivable from given summary statistics. We show that this task is easy if data is additive and the set of given summary statistics can be modelled by a graph.

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Author information

Authors and Affiliations

  1. Computer Science Department, “La Sapienza” University of Rome, Italy

    Francesco M. Malvestuto & Mauro Mezzini

Authors
  1. Francesco M. Malvestuto
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  2. Mauro Mezzini
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007, Tarragona, Catalonia

    Josep Domingo-Ferrer

  2. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

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© 2004 Springer-Verlag Berlin Heidelberg

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Malvestuto, F.M., Mezzini, M. (2004). Privacy Preserving and Data Mining in an On-Line Statistical Database of Additive Type. In: Domingo-Ferrer, J., Torra, V. (eds) Privacy in Statistical Databases. PSD 2004. Lecture Notes in Computer Science, vol 3050. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25955-8_29

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  • DOI: https://doi.org/10.1007/978-3-540-25955-8_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22118-0

  • Online ISBN: 978-3-540-25955-8

  • eBook Packages: Springer Book Archive

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