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In big data era, massive and high-dimensional data is produced at all times, increasing the difficulty of analyzing and protecting data. In this paper, in order to realize dimensionality reduction and privacy protection of data, principal component analysis (PCA) and differential privacy (DP) are combined to handle these data. Moreover, support vector machine (SVM) is used to measure the availability of processed data in our paper. Specifically, we introduced differential privacy mechanisms at different stages of the algorithm PCA-SVM and obtained the algorithms DPPCA-SVM and PCADP-SVM. Both algorithms satisfy ε , 0 -DP while achieving fast classification. In addition, we evaluate the performance of two algorithms in terms of noise expectation and classification accuracy from the perspective of theoretical proof and experimental verification. To verify the performance of DPPCA-SVM, we also compare our DPPCA-SVM with other algorithms. Results show that DPPCA-SVM provides excellent utility for different data sets despite guaranteeing stricter privacy.

Abstract-With the development of the Internet, the amount of information is expanding rapidly. Naturally, search engine becomes the backbone of information management. Nevertheless, the flooding of large number of malicious websites on search engine has posed tremendous threat to our users. Most of exiting systems to detect malicious websites focus on specific attack. At the same time, available browser extensions based on blacklist are powerless to countless websites. In this paper, we present a lightweight approach using static analysis techniques to quickly discriminate malicious sites comprising malware, drive-by-download and phishing sites. We extract comprehensive features to classify labeled dataset using various machine learning algorithms. Large scale evaluation of our dataset shows that the classification accuracy reaches 97.5% with low overhead. Furthermore, we achieved a chrome plugin to detect malicious search result websites based on our classification model.