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- Title
Privacy Preserving RBF Kernel Support Vector Machine.
- Authors
Haoran Li; Li Xiong; Ohno-Machado, Lucila; Xiaoqian Jiang
- Abstract
Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data.
- Publication
BioMed Research International, 2014, Vol 2014, p1
- ISSN
2314-6133
- Publication type
Article
- DOI
10.1155/2014/827371