Privacy-preserving outsourcing support vector machines with random transformation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
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Support vector machine (SVM) is a widely used tool in classification problem. SVM solves a quadratic optimization problem to decide which instances of training dataset are support vectors, i.e., the necessarily informative instances to form the classifier. The support vectors are intact tuples taken from the training dataset. Releasing the SVM classifier to public use or shipping the SVM classifier to clients will disclose the private content of support vectors, violating the privacy-preservation requirement in some legal or commercial reasons. To the best of our knowledge, there has not been work extending the notion of privacy-preservation to releasing the SVM classifier. In this paper, we propose an approximation approach which post-processes the SVM classifier to protect the private content of support vectors. This approach is designed for the commonly used Gaussian radial basis function kernel. By applying this post-processor on the SVM classifier, the resulted privacy-preserving SVM classifier can be publicly released without exposing the private content of support vectors and is able to provide comparable classification accuracy to the original SVM classifier.