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Efficient Unconditionally Secure Comparison and Privacy Preserving Machine Learning Classification Protocols

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Abstract

We propose an efficient unconditionally secure protocol for privacy preserving comparison of \(\ell \)-bit integers when both integers are shared between two semi-honest parties. Using our comparison protocol as a building block, we construct two-party generic private machine learning classifiers. In this scenario, one party holds an input while the other holds a model and they wish to classify the input according to the model without revealing their private information to each other. Our constructions are based on the setup assumption that there exists pre-distributed correlated randomness available to the computing parties, the so-called commodity-based model. The protocols are storage and computationally efficient, consisting only of additions and multiplications of integers.

Bernardo David was supported by European Research Council Starting Grant 279447. The author acknowledges support from the Danish National Research Foundation and The National Science Foundation of China (under the grant 61061130540) for the Sino-Danish Center for the Theory of Interactive Computation, and also from the CFEM research centre (supported by the Danish Strategic Research Council) within which part of this work was performed.

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Author information

Authors and Affiliations

  1. Aarhus University, Aarhus, Denmark

    Bernardo David

  2. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Rafael Dowsley

  3. University of Washington Tacoma, Tacoma, USA

    Raj Katti & Anderson C. A. Nascimento

Authors
  1. Bernardo David
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  2. Rafael Dowsley
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  3. Raj Katti
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  4. Anderson C. A. Nascimento
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Corresponding author

Correspondence to Rafael Dowsley .

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Editors and Affiliations

  1. The Hong Kong Polytechnic University, Hong Kong, China

    Man-Ho Au

  2. Japan Advanced Inst. of Science and Tech, Ishikawa, Japan

    Atsuko Miyaji

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David, B., Dowsley, R., Katti, R., Nascimento, A.C.A. (2015). Efficient Unconditionally Secure Comparison and Privacy Preserving Machine Learning Classification Protocols. In: Au, MH., Miyaji, A. (eds) Provable Security. ProvSec 2015. Lecture Notes in Computer Science(), vol 9451. Springer, Cham. https://doi.org/10.1007/978-3-319-26059-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-26059-4_20

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