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A new privacy-preserving proximal support vector machine for classification of vertically partitioned data

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Abstract

A new privacy-preserving proximal support vector machine (P3SVM) is formulated for classification of vertically partitioned data. Our classifier is based on the concept of global random reduced kernel which is composed of local reduced kernels. Each of them is computed using local reduced matrix with Gaussian perturbation, which is privately generated by only one of the parties, and never made public. This formulation leads to an extremely simple and fast privacy-preserving algorithm, for generating a linear or nonlinear classifier that merely requires the solution of a single system of linear equations. Comprehensive experiments are conducted on multiple publicly available benchmark datasets to evaluate the performance of the proposed algorithms and the results indicate that: (a) Our P3SVM achieves better performance than the recently proposed privacy-preserving SVM via random kernels in terms of both classification accuracy and computational time. (b) A significant improvement of accuracy is attained by our P3SVM when compared to classifiers generated only using each party’s own data. (c) The generated classifier has comparable accuracy to an ordinary PSVM classifier trained on the entire dataset, without releasing any private data.

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Acknowledgments

This work is supported by the China Agricultural Research System (CARS-30).

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Authors and Affiliations

  1. College of Science, Applied Mathematics, China Agricultural University, Beijing, China

    Li Sun, Biao Qi & Zhi-Jian Zhou

  2. College of Information and Electrical Engineering, China Agricultural University, Beijing, China

    Wei-Song Mu

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  1. Li Sun
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  2. Wei-Song Mu
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  3. Biao Qi
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Correspondence to Zhi-Jian Zhou.

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Sun, L., Mu, WS., Qi, B. et al. A new privacy-preserving proximal support vector machine for classification of vertically partitioned data. Int. J. Mach. Learn. & Cyber. 6, 109–118 (2015). https://doi.org/10.1007/s13042-014-0245-1

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  • DOI: https://doi.org/10.1007/s13042-014-0245-1

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