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Privacy Preserving Frequency-Based Learning Algorithms in Two-Part Partitioned Record Model

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Knowledge and Systems Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 245))

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Abstract

In this paper, we consider a new scenario for privacy-preserving data mining called two-part partitioned record model (TPR) and find solutions for a family of frequency-based learning algorithms in TPR model. In TPR, the dataset is distributed across a large number of users in which each record is owned by two different users, one user only knows the values for a subset of attributes and the other knows the values for the remaining attributes. A miner aims to learn, for example, classification rules on their data, while preserving each user’s privacy. In this work we develop a cryptographic solution for frequency-based learning methods in TPR. The crucial step in the proposed solution is the privacy-preserving computation of frequencies of a tuple of values in the users’ data, which can ensure each user’s privacy without loss of accuracy.We illustrate the applicability of the method by using it to build the privacy preserving protocol for the naive Bayes classifier learning, and briefly address the solution in other applications. Experimental results show that our protocol is efficient.

An erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-319-02821-7_39

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-02821-7_39

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Author information

Authors and Affiliations

  1. Academy of Cryotographic Techniques, Hanoi, Vietnam

    The Dung Luong

  2. Hanoi National University of Education, Hanoi, Vietnam

    Dang Hung Tran

Authors
  1. The Dung Luong
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  2. Dang Hung Tran
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Corresponding author

Correspondence to The Dung Luong .

Editor information

Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Van Nam Huynh

  2. UMR CNRS 7253 Heudiasyc, University of Technology of Compiegne, Compiegne Cedex, France

    Thierry Denoeux

  3. Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam

    Dang Hung Tran

  4. Faculty of Information Technology, University of Engineering and Technology, Hanoi, Vietnam

    Anh Cuong Le

  5. Faculty of Information Technology, University of Engineering and Technology, Hanoi, Vietnam

    Son Bao Pham

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© 2014 Springer International Publishing Switzerland

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Luong, T.D., Tran, D.H. (2014). Privacy Preserving Frequency-Based Learning Algorithms in Two-Part Partitioned Record Model. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 245. Springer, Cham. https://doi.org/10.1007/978-3-319-02821-7_29

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  • DOI: https://doi.org/10.1007/978-3-319-02821-7_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02820-0

  • Online ISBN: 978-3-319-02821-7

  • eBook Packages: EngineeringEngineering (R0)

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