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A Review of Privacy-Preserving Machine Learning Classification

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11066))

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Abstract

Machine Learning (ML) Classification has already become one of the most commonly used techniques in many areas such as banking, medicine, spam detection and data mining applications. Often, the training of models require massive data which may contain sensitive information and the classification phase may expose the train models and the inputs from the users. Neither the models nor the train datasets and inputs should expose private information. Addressing this goal, several schemes have been proposed for privacy preserving classification. In this paper, we review those privacy preserving techiniques which applied for different machine learning classification algorithms. These algorithms conclude k-NN, SVM, Bayesian, neural networks, decision tree and etc. we sum up the comparison protocols. Finally, this work comes up with some correlative problems which are worthy to study in the future.

Supported by Fundamental Research Funds for the Central Universities (N171704005) and Shenyang Science and Technology Plan Projects (18-013-0-01).

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Acknowledgements

Supported by the National Natural Science Foundation of China (61872069), Fundamental Research Funds for the Central Universities (N171704005) and Shenyang Science and Technology Plan Projects (18-013-0-01).

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Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, 110169, China

    Andy Wang, Chen Wang, Meng Bi & Jian Xu

  2. State Key Laboratory of Information Security (Institute of Information Engineering, The Chinese Academy of Sciences), Beijing, 100093, China

    Jian Xu

  3. Shenyang University of Technology, Shenyang, 110023, China

    Meng Bi

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  1. Andy Wang
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  2. Chen Wang
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  4. Jian Xu
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Corresponding author

Correspondence to Jian Xu .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Wang, A., Wang, C., Bi, M., Xu, J. (2018). A Review of Privacy-Preserving Machine Learning Classification. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11066. Springer, Cham. https://doi.org/10.1007/978-3-030-00015-8_58

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  • DOI: https://doi.org/10.1007/978-3-030-00015-8_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00014-1

  • Online ISBN: 978-3-030-00015-8

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