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International Conference on Computational Science and Its Applications

ICCSA 2014: Computational Science and Its Applications – ICCSA 2014 pp 695–708Cite as

Privacy-Preserving Kriging Interpolation on Distributed Data

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8584))

Abstract

Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. The accuracy of the kriging model depends on the total number of measured points. Acquiring sufficient number of measurement requires so much time and budget. In some scenarios, private or governmental institutions may collect geostatistical data for the same or neighbor region. Collaboration of such organizations may build better models, if they join their data sets. However, due to financial and privacy reasons, they might hesitate to collaborate.

In this study, we propose a solution to build kriging model using distributed data while preserving privacy of each data owners and the client that requests prediction. The proposed scheme creates a kriging model on joint data of all parties who wants to collaborate. We analyze our solution with respect to privacy, performance, and accuracy. Our solution has extra costs; however, they are not that critical. We conduct experiments on real data sets to show that our scheme gives better result than the model created on insufficient measured data.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Ankara University, 06100, Ankara, Turkey

    Bulent Tugrul

  2. Computer Engineering Department, Anadolu University, 26470, Eskisehir, Turkey

    Huseyin Polat

Authors
  1. Bulent Tugrul
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  2. Huseyin Polat
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Editor information

Editors and Affiliations

  1. School of Engineering, University of Basilicata, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

  3. Department of Production and Systems, University of Minho, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  4. DICAR, Polytecnico di Bari, 70125, Bari, Italy

    Carmelo Torre

  5. University of Minho, Braga, Portugal

    Jorge Gustavo Rocha  & Maria Irene Falcão  & 

  6. Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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© 2014 Springer International Publishing Switzerland

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Tugrul, B., Polat, H. (2014). Privacy-Preserving Kriging Interpolation on Distributed Data. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_52

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  • DOI: https://doi.org/10.1007/978-3-319-09153-2_52

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09152-5

  • Online ISBN: 978-3-319-09153-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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