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2006
ACM

Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data

9 years 1 months ago
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the non-disclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from the data distributed at multiple parties, without disclosing the data of each party to others. We assume that data is horizontally partitioned – each party collects the ...
Hwanjo Yu, Xiaoqian Jiang, Jaideep Vaidya
Added 14 Jun 2010
Updated 14 Jun 2010
Type Conference
Year 2006
Where SAC
Authors Hwanjo Yu, Xiaoqian Jiang, Jaideep Vaidya
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