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.
Keywords
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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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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