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Erschienen in:
2017 | OriginalPaper | Buchkapitel
An Efficient Privacy-Preserving Classification Method with Condensed Information
verfasst von : Xinning Li, Zhiping Zhou
Erschienen in: Image and Graphics
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Abstract
Privacy-preserving is a challenging problem in real-world data classification. Among the existing classification methods, the support vector machine (SVM) is a popular approach which has a high generalization ability. However, when datasets are privacy and complexity, the processing capacity of SVM is not satisfactory. In this paper, we propose a new method CI-SVM to achieve efficient privacy-preserving of the SVM. On the premise of ensuring the accuracy of classification, we condense the original dataset by a new method, which transforms the privacy information to condensed information with little additional calculation. The condensed information carries the class characteristics of the original information and doesn’t expose the detailed original data. The time-consuming of classification is greatly reduced because of the fewer samples as condensed information. Our experiment results on datasets show that the proposed CI-SVM algorithm has obvious advantages in classification efficiency.