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Found 4 papers, 1 papers with code
Private Text Classification with Convolutional Neural Networks
Text classifiers are regularly applied to personal texts, leaving users of these classifiers vulnerable to privacy breaches.
PrivFair: a Library for Privacy-Preserving Fairness Auditing
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance.
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.
