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A Novel Anti-classification Approach for Knowledge Protection

  • Transactional Processing Systems
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Abstract

Classification is the problem of identifying a set of categories where new data belong, on the basis of a set of training data whose category membership is known. Its application is wide-spread, such as the medical science domain. The issue of the classification knowledge protection has been paid attention increasingly in recent years because of the popularity of cloud environments. In the paper, we propose a Shaking Sorted-Sampling (triple-S) algorithm for protecting the classification knowledge of a dataset. The triple-S algorithm sorts the data of an original dataset according to the projection results of the principal components analysis so that the features of the adjacent data are similar. Then, we generate noise data with incorrect classes and add those data to the original dataset. In addition, we develop an effective positioning strategy, determining the added positions of noise data in the original dataset, to ensure the restoration of the original dataset after removing those noise data. The experimental results show that the disturbance effect of the triple-S algorithm on the CLC, MySVM, and LibSVM classifiers increases when the noise data ratio increases. In addition, compared with existing methods, the disturbance effect of the triple-S algorithm is more significant on MySVM and LibSVM when a certain amount of the noise data added to the original dataset is reached.

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Acknowledgments

This work was supported partially by the Ministry of Science and Technology of Republic of China under grant MOST 103-2221-E-025-007 and 102-2218-E-025-001.

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Authors and Affiliations

  1. Department of Information Management, National Taichung University of Science and Technology, Taichung City, Taiwan

    Chen-Yi Lin

  2. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung City, Taiwan

    Tung-Shou Chen

  3. School of Medical Laboratory and Biotechnology, Chung Shan Medical University, Taichung City, Taiwan

    Hui-Fang Tsai

  4. Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Taiwan

    Wei-Bin Lee & Yuan-Hung Kao

  5. Department of Operation, Visitor Service, Collection and Information Manage, National Museum of Natural Science, Taichung City, Taiwan

    Tien-Yu Hsu & Yuan-Hung Kao

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  1. Chen-Yi Lin
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  2. Tung-Shou Chen
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Correspondence to Yuan-Hung Kao.

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This article is part of the Topical Collection on Transactional Processing Systems

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Lin, CY., Chen, TS., Tsai, HF. et al. A Novel Anti-classification Approach for Knowledge Protection. J Med Syst 39, 113 (2015). https://doi.org/10.1007/s10916-015-0305-4

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