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01-10-2015 | Transactional Processing Systems

A Novel Anti-classification Approach for Knowledge Protection

Authors: Chen-Yi Lin, Tung-Shou Chen, Hui-Fang Tsai, Wei-Bin Lee, Tien-Yu Hsu, Yuan-Hung Kao

Published in: Journal of Medical Systems | Issue 10/2015

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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Title: A Novel Anti-classification Approach for Knowledge Protection
Authors: Chen-Yi Lin
Tung-Shou Chen
Hui-Fang Tsai
Wei-Bin Lee
Tien-Yu Hsu
Yuan-Hung Kao
Publication date: 01-10-2015
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 10/2015
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-015-0305-4

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