Abstract
As increasing amounts of sensitive personal information is aggregated into data repositories, it has become important to develop mechanisms for processing the data without revealing information about individual data instances. The differential privacy model provides a framework for the development and theoretical analysis of such mechanisms. In this paper, we propose an algorithm for learning a discriminatively trained multiclass Gaussian classifier that satisfies differential privacy using a large margin loss function with a perturbed regularization term. We present a theoretical upper bound on the excess risk of the classifier introduced by the perturbation.
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Pathak, M.A., Raj, B. (2011). Large Margin Multiclass Gaussian Classification with Differential Privacy. In: Dimitrakakis, C., Gkoulalas-Divanis, A., Mitrokotsa, A., Verykios, V.S., Saygin, Y. (eds) Privacy and Security Issues in Data Mining and Machine Learning. PSDML 2010. Lecture Notes in Computer Science(), vol 6549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19896-0_9
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DOI: https://doi.org/10.1007/978-3-642-19896-0_9
Publisher Name: Springer, Berlin, Heidelberg
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