Privacy preserving RBF kernel support vector machine.

TitlePrivacy preserving RBF kernel support vector machine.
Publication TypeJournal Article
Year of Publication2014
AuthorsLi, H, Xiong, L, Ohno-Machado, L, Jiang, X
JournalBiomed Res Int
Volume2014
Pagination827371
Date Published2014
ISSN2314-6141
iDASH CategoryPrivacy Technology
Abstract<p>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.</p>
DOI10.1155/2014/827371
Alternate JournalBiomed Res Int
PubMed ID25013805
PubMed Central IDPMC4071990
Grant ListR00LM011392 / LM / NLM NIH HHS / United States
U54HL108460 / HL / NHLBI NIH HHS / United States