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IFIP International Conference on Communications and Multimedia Security

CMS 2012: Communications and Multimedia Security pp 192–194Cite as

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Predicate-Tree Based Pretty Good Privacy of Data

Predicate-Tree Based Pretty Good Privacy of Data

  • William Perrizo18 &
  • Arjun G. Roy18 
  • Conference paper
  • 920 Accesses

Part of the Lecture Notes in Computer Science book series (LNSC,volume 7394)

Abstract

Growth of Internet has led to exponential rise in data communication over the World Wide Web. Several applications and entities such as online banking transactions, stock trading, e-commerce Web sites, etc. are at a constant risk of eavesdropping and hacking. Hence, security of data is of prime concern. Recently, vertical data have gained lot of focus because of their significant performance benefits over horizontal data in various data mining applications. In our current work, we propose a Predicate-Tree based solution for protection of data. Predicate-Trees or pTrees are compressed, data-mining-ready, vertical data structures and have been used in a plethora of data-mining research areas such as spatial association rule mining, text clustering, closed k-nearest neighbor classification, etc. We show how for data mining purposes, the scrambled pTrees would be unrevealing of the raw data to anyone except for the authorized person issuing a data mining request. In addition, we propose several techniques which come along as a benefit of using vertical pTrees. To the best of our knowledge, our approach is novel and provides sufficient speed and protection level for an effective data security.

Keywords

  • Predicate Trees
  • Data Mining
  • Data Security

We acknowledge partial financial support for this research from a Department of Energy Award (award # DE-FG52-08NA28921).

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References

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  2. Khan, M., Ding, Q., Perrizo, W.: k-nearest Neighbor Classification on Spatial Data Streams Using P-trees. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 517–528. Springer, Heidelberg (2002)

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  3. Rahal, I., Perrizo, W.: An optimized approach for KNN text categorization using P-trees. In: ACM Symposium on Applied Computing, pp. 613–617 (2004)

    Google Scholar 

  4. Perrizo, W.: Predicate Count Tree Technology. Technical Report NDSU-CSOR-TR-01-1 (2001)

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  5. Wang, Y., Lu, T., Perrizo, W.: A Novel Combinatorial Score for Feature Selection with P-Tree in DNA Microarray Data Analysis. In: 19th International Conference on Software Engineering and Data Engineering, pp. 295–300 (2010)

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Author information

Authors and Affiliations

  1. Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND, 58102, USA

    William Perrizo & Arjun G. Roy

Authors
  1. William Perrizo
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  2. Arjun G. Roy
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Editor information

Editors and Affiliations

  1. Department of Computer Science, IBBT-DistriNet, K.U. Leuven, Celestijnenlaan 200A, 3001, Leuven, Belgium

    Bart De Decker

  2. School of Computing, University of Kent, CT2 7NZ, Canterbury, Kent, UK

    David W. Chadwick

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© 2012 IFIP International Federation for Information Processing

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Cite this paper

Perrizo, W., Roy, A.G. (2012). Predicate-Tree Based Pretty Good Privacy of Data. In: De Decker, B., Chadwick, D.W. (eds) Communications and Multimedia Security. CMS 2012. Lecture Notes in Computer Science, vol 7394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32805-3_16

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  • DOI: https://doi.org/10.1007/978-3-642-32805-3_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32804-6

  • Online ISBN: 978-3-642-32805-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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