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Private Text Classification with Convolutional Neural Networks
Text classifiers are regularly applied to personal texts, leaving users of these classifiers vulnerable to privacy breaches.
Privacy-Preserving Training of Tree Ensembles over Continuous Data
In this paper we propose three more efficient alternatives for secure training of decision tree based models on data with continuous features, namely: (1) secure discretization of the data, followed by secure training of a decision tree over the discretized data; (2) secure discretization of the data, followed by secure training of a random forest over the discretized data; and (3) secure training of extremely randomized trees (``extra-trees'') on the original data.
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