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A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data

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Book cover Privacy-Preserving Data Mining

Part of the book series: Advances in Database Systems ((ADBS,volume 34))

The goal of data mining is to extract or “mine” knowledge from large amounts of data. However, data is often collected by several different sites. Privacy, legal and commercial concerns restrict centralized access to this data, thus derailing data mining projects. Recently, there has been growing focus on finding solutions to this problem. Several algorithms have been proposed that do distributed knowledge discovery, while providing guarantees on the non-disclosure of data. Vertical partitioning of data is an important data distribution model often found in real life. Vertical partitioning or heterogeneous distribution implies that different features of the same set of data are collected by different sites. In this chapter we survey some of the methods developed in the literature to mine vertically partitioned data without violating privacy and discuss challenges and complexities specific to vertical partitioning.

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Author information

Authors and Affiliations

  1. MSIS Department and CIMIC, Rutgers University, Clarion, PA, USA

    Jaideep Vaidya

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  1. Jaideep Vaidya
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Editor information

Editors and Affiliations

  1. IBM Thomas J. Watson Research Center, 19 Skyline Drive, 10532, Hawthorne, NY, USA

    Charu C. Aggarwal

  2. Department of Computer Science, University of Illinois at Chicago, 854 South Morgan Street, 60607-7053, Chicago, IL, USA

    Philip S. Yu

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Vaidya, J. (2008). A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data. In: Aggarwal, C.C., Yu, P.S. (eds) Privacy-Preserving Data Mining. Advances in Database Systems, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-70992-5_14

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  • DOI: https://doi.org/10.1007/978-0-387-70992-5_14

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-70991-8

  • Online ISBN: 978-0-387-70992-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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