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Found 4 papers, 1 papers with code
Training Differentially Private Models with Secure Multiparty Computation
We address the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal privacy guarantees regarding the protection of each owner's data.
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.
Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation
We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification.
High Performance Logistic Regression for Privacy-Preserving Genome Analysis
In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function.
