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Open AccessArticle

Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

Department of Physics and Electronics, National University of Lesotho, P.O. Roma 180, Maseru 100, Lesotho
Mach. Learn. Knowl. Extr. 2019, 1(1), 483-491; https://doi.org/10.3390/make1010029
Received: 30 December 2018 / Revised: 13 February 2019 / Accepted: 19 February 2019 / Published: 20 February 2019
(This article belongs to the Section Privacy)
The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification. View Full-Text
Keywords: support vector machine; differential privacy; privacy; privacy-preserving machine learning; Modified National Institute of Standards and Technology (MNIST); image classification support vector machine; differential privacy; privacy; privacy-preserving machine learning; Modified National Institute of Standards and Technology (MNIST); image classification
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Senekane, M. Differentially Private Image Classification Using Support Vector Machine and Differential Privacy. Mach. Learn. Knowl. Extr. 2019, 1, 483-491.

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