The ubiquity of data, including multi-media data such as images, enables easy mining and\nanalysis of such data. However, such an analysis might involve the use of sensitive data such as medical\nrecords (including radiological images) and financial records. Privacy-preserving machine learning is\nan approach that is aimed at the analysis of such data in such a way that privacy is not compromised.\nThere are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness\nand Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis\ndue to its robustness against background knowledge attacks. In this paper, we report a scheme for\nprivacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as\na classification algorithm because unlike variants of artificial neural networks, it converges to a global\noptimum. SVM kernels used are linear and Radial Basis Function (RBF), while.............
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