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An Analysis of Privacy Preservation Techniques in Data Mining

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Advances in Computing and Information Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 178))

Abstract

Data mining techniques are used extensively for deducting the implicit, previously unknown, and potentially useful information from large data sets by using statistical and intelligent methodologies. The deduction of patterns or conclusions may uncover the information that may sometimes compromise the confidentiality and privacy obligations. Preservation of privacy is an important aspect of data mining and thus study of achieving some data mining goals without sacrificing the privacy of the individuals is not only demanding but also a task of practical importance. The analysis of privacy preserving data mining (PPDM) algorithms should consider the effects of these algorithms in mining the results as well as in preserving privacy. The privacy should be preserved in all the three aspects of mining as association rules, classifiers and clusters. In this paper we present a review of the commonly existing efficient methodologies in the context of privacy preservation in data mining.

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Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, Maulana Azad National Institute of Technology, Bhopal, India

    Abhishek Sachan & Devshri Roy

  2. Department of Geomatics, Maulana Azad National Institute of Technology, Bhopal, India

    P. V. Arun

Authors
  1. Abhishek Sachan
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  2. Devshri Roy
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  3. P. V. Arun
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Corresponding author

Correspondence to Abhishek Sachan .

Editor information

Editors and Affiliations

  1. , Department of Computer Science, Jackson State University, John R. Lynch Street 1400, Jackson, 39217, Mississippi, USA

    Natarajan Meghanathan

  2. Wireilla Net Solutions PTY ltd, Merlow Street 3, Melbourne, 3207, Australia

    Dhinaharan Nagamalai

  3. , Department of Computer Science & Eng., University of Calcutta, Calcutta, 700 073, India

    Nabendu Chaki

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Sachan, A., Roy, D., Arun, P.V. (2013). An Analysis of Privacy Preservation Techniques in Data Mining. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31600-5_12

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  • DOI: https://doi.org/10.1007/978-3-642-31600-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31599-2

  • Online ISBN: 978-3-642-31600-5

  • eBook Packages: EngineeringEngineering (R0)

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