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Secure Multi-party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9490))

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Abstract

Especially in the Big Data era, the usage of different classification methods is increasing day by day. The success of these classification methods depends on the effectiveness of learning methods. Extreme learning machine (ELM) classification algorithm is a relatively new learning method built on feed-forward neural-network. ELM classification algorithm is a simple and fast method that can create a model from high-dimensional data sets. Traditional ELM learning algorithm implicitly assumes complete access to whole data set. This is a major privacy concern in most of cases. Sharing of private data (i.e. medical records) is prevented because of security concerns. In this research, we propose an efficient and secure privacy-preserving learning algorithm for ELM classification over data that is vertically partitioned among several parties. The new learning method preserves the privacy on numerical attributes, builds a classification model without sharing private data without disclosing the data of each party to others.

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Author information

Authors and Affiliations

  1. TÜBİTAK BİLGEM, Cyber Security Institute, Gebze, Kocaeli, Turkey

    Ferhat Özgür Çatak

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  1. Ferhat Özgür Çatak
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Correspondence to Ferhat Özgür Çatak .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Çatak, F.Ö. (2015). Secure Multi-party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_39

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  • DOI: https://doi.org/10.1007/978-3-319-26535-3_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26534-6

  • Online ISBN: 978-3-319-26535-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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