Efficient Data Classification with Privacy-Preservation
Date Issued
2011
Date
2011
Author(s)
Lin, Keng-Pei
Abstract
Data classification is a widely used data mining technique which learns classifiers from labeled data to predict the labels of unlabeled instances. Among data classification algorithms, the support vector machine (SVM) shows the state-of-the-art performance. Data privacy is a critical concern in applying the data mining techniques. In this dissertation, we study how to achieve privacy-preservation in utilizing the SVM as well as how to efficiently generate the SVM classifier.
Outsourcing has become popular in current cloud computing trends. Since the training algorithm of the SVM involves intensive computations, outsourcing to external service providers can benefit the data owner who possesses only limited computing resources. In outsourcing, the data privacy is a critical concern since there may be sensitive information contained in the data. In addition to the data, the classifier generated from the data is also private to the data owner. Existing privacy-preserving SVM outsourcing technique is weak in security. In Chapter 2, we propose a secure privacy-preserving SVM outsourcing scheme. In the proposed scheme, the data are perturbed by random linear transformation which is stronger in security than existing works. The service provider generates the SVM classifier from the perturbed data where the classifier is also in perturbed form and cannot be accessed by the service provider.
In Chapter 3, we study the inherent privacy violation problem in the SVM classifier. The SVM trains a classifier by solving an optimization problem to decide which instances of the training dataset are support vectors, which are the necessarily informative instances to form the SVM classifier. Since support vectors are intact tuples taken from the training dataset, releasing the SVM classifier for public use or other parties will disclose the private content of support vectors. We propose an approach to post-process the SVM classifier to transform it to a privacy-preserving SVM classifier which does not disclose the private content of support vectors. It precisely approximates the decision function of the Gaussian kernel SVM classifier without exposing the individual content of support vectors. The privacy-preserving SVM classifier is able to release the prediction ability of the SVM classifier without violating the individual data privacy.
The efficiency of the SVM is also an important issue since for large-scale data, the SVM solver converges slowly. In Chapter 4, we design an efficient SVM training algorithm based on the kernel approximation technique developed in Chapter 3. The kernel function brings powerful classification ability to the SVM, but it incurs additional computational cost in the training process. In contrast, there exist faster solvers to train the linear SVM.
We capitalize the kernel approximation technique to compute the kernel evaluation by the dot product of explicit low-dimensional features to leverage the efficient linear SVM solver for training a nonlinear kernel SVM. In addition to an efficient training scheme, it obtains a privacy-preserving SVM classifier directly, i.e., its classifier does not disclose any individual instance.
We conduct extensive experiments over our studies. Experimental results show that the privacy-preserving SVM outsourcing scheme, the privacy-preserving SVM classifier, and the efficient SVM training scheme based on kernel approximation achieve similar classification accuracy to a normal SVM classifier while obtains the properties of privacy-preservation and efficiency respectively.
Subjects
Data mining
classification
support vector machines
kernel function
privacy-preserving
outsourcing
Type
thesis
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