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Non-black-Box Computation of Linear Regression Protocols with Malicious Adversaries

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Information Security Practice and Experience (ISPEC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 6672))

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Abstract

Linear regression is a basic statistical method to correlate two or more attributes in data mining, machine learning, decision tree and Bayes classification. This paper studies non-black-box two-party computation of linear regression protocols with malicious adversaries. The contribution of this paper comprises the following three-fold:

  • in the first fold, a general two-party computation model for linear regression protocols is introduced and formalized;

  • in the second fold, a non-black-box two-party computation of linear regression protocols based on the Goldreich, Micali and Wigderson’s compiler technique is presented;

  • in the third fold, we show that the proposed non-black-box construction tolerates malicious adversaries in the simulation-based framework assuming that the underlying Damgård and Jurik’s public key encryption scheme is semantically secure and the Damgård-Fujisaki commitment scheme is statistically hiding and computationally binding.

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Author information

Authors and Affiliations

  1. I2R, A*STAR, Singapore

    Huafei Zhu

Authors
  1. Huafei Zhu
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Editor information

Editors and Affiliations

  1. Institute for Infocomm Research, 1 Fusionopolis Way, # 19-01 Connexis (South Tower), 138632, Singapore, Singapore

    Feng Bao

  2. Jinan University, Huangpu Avenue West 602, 510632, Tianhe District, Guangzhou, China

    Jian Weng

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Zhu, H. (2011). Non-black-Box Computation of Linear Regression Protocols with Malicious Adversaries. In: Bao, F., Weng, J. (eds) Information Security Practice and Experience. ISPEC 2011. Lecture Notes in Computer Science, vol 6672. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21031-0_28

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  • DOI: https://doi.org/10.1007/978-3-642-21031-0_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21030-3

  • Online ISBN: 978-3-642-21031-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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