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A Fast Secure Dot Product Protocol with Application to Privacy Preserving Association Rule Mining

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Advances in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8443))

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Abstract

Data mining often causes privacy concerns. To ease the concerns, various privacy preserving data mining techniques have been proposed. However, those techniques are often too computationally intensive to be deployed in practice. Efficiency becomes a major challenge in privacy preserving data mining. In this paper we present an efficient secure dot product protocol and show its application in privacy preserving association rule mining, one of the most widely used data mining techniques. The protocol is orders of magnitude faster than previous protocols because it employs mostly cheap cryptographic operations, e.g. hashing and modular multiplication. The performance has been further improved by parallelization. We implemented the protocol and tested the performance. The test result shows that on moderate commodity hardware, the dot product of two vectors of size 1 million can be computed within 1 minute. As a comparison, the currently most widely used protocol needs about 1 hour and 23 minutes.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK

    Changyu Dong

  2. Hewlett-Packard Laboratories, Bristol, UK

    Liqun Chen

Authors
  1. Changyu Dong
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  2. Liqun Chen
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Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan, R.O.C.

    Vincent S. Tseng  & Hung-Yu Kao  & 

  2. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Tu Bao Ho

  3. Nanjing University, China

    Zhi-Hua Zhou

  4. National Chengchi University, Taipei, Taiwan, R.O.C.

    Arbee L. P. Chen

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Dong, C., Chen, L. (2014). A Fast Secure Dot Product Protocol with Application to Privacy Preserving Association Rule Mining. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_50

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  • DOI: https://doi.org/10.1007/978-3-319-06608-0_50

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06607-3

  • Online ISBN: 978-3-319-06608-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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