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Privacy Preserving Protocols for Eigenvector Computation

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Privacy and Security Issues in Data Mining and Machine Learning (PSDML 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6549))

Abstract

In this paper, we present a protocol for computing the principal eigenvector of a collection of data matrices belonging to multiple semi-honest parties with privacy constraints. Our proposed protocol is based on secure multi-party computation with a semi-honest arbitrator who deals with data encrypted by the other parties using an additive homomorphic cryptosystem. We augment the protocol with randomization and oblivious transfer to make it difficult for any party to estimate properties of the data belonging to other parties from the intermediate steps. The previous approaches towards this problem were based on expensive QR decomposition of correlation matrices, we present an efficient algorithm using the power iteration method. We present an analysis of the correctness, security, and efficiency of protocol.

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Author information

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Manas Pathak & Bhiksha Raj

Authors
  1. Manas Pathak
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  2. Bhiksha Raj
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Editor information

Editors and Affiliations

  1. Johann Wolfgang Goethe University, Ruth-Moufang-Str. 1, 60438, Frankfurt am Main, Germany

    Christos Dimitrakakis

  2. Information Analytics Lab, IBM Research – Zurich, Säumerstrasse 4, 8803, Rüschlikon, Switzerland

    Aris Gkoulalas-Divanis

  3. Ecole Polytechnice Fédérale de Lausanne, I&C - ISC - LASEC, Bâtiment INF, Station 14, CH-1015, Lausanne, Switzerland

    Aikaterini Mitrokotsa

  4. Department of Computer and Communication Engineering, University of Thessaly, Glavani 37 & 28TH, GR 38221, Octovriou, Volos, Greece

    Vassilios S. Verykios

  5. Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli, 34956, Tuzla, Istanbul, Turkey

    Yücel Saygin

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Pathak, M., Raj, B. (2011). Privacy Preserving Protocols for Eigenvector Computation. In: Dimitrakakis, C., Gkoulalas-Divanis, A., Mitrokotsa, A., Verykios, V.S., Saygin, Y. (eds) Privacy and Security Issues in Data Mining and Machine Learning. PSDML 2010. Lecture Notes in Computer Science(), vol 6549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19896-0_10

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  • DOI: https://doi.org/10.1007/978-3-642-19896-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19895-3

  • Online ISBN: 978-3-642-19896-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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