Privacy preserving RBF kernel support vector machine
Autor: | Lucila Ohno-Machado, Haoran Li, Li Xiong, Xiaoqian Jiang |
---|---|
Rok vydání: | 2014 |
Předmět: |
Support Vector Machine
Article Subject Computer science Medical Informatics Computing lcsh:Medicine 02 engineering and technology Machine learning computer.software_genre General Biochemistry Genetics and Molecular Biology Software Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Differential privacy Humans Dissemination General Immunology and Microbiology business.industry Information Dissemination lcsh:R General Medicine Privacy preserving Data sharing Support vector machine Privacy Radial basis function kernel 020201 artificial intelligence & image processing Noise (video) Artificial intelligence Health Services Research business computer Algorithms Research Article |
Zdroj: | BioMed Research International BioMed Research International, Vol 2014 (2014) |
ISSN: | 2314-6141 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |