5 citations · 6 across the 2 of their papers we have counts for
4 papers
Training Differentially Private Models with Secure Multiparty Computation
Sikha Pentyala, Davis Railsback, Ricardo Maia +4
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 prote…
Privacy-Preserving Training of Tree Ensembles over Continuous Data
Samuel Adams, Chaitali Choudhary, Martine De Cock +5
Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving training of decision trees over distributed data assume that the features are categorical. In re…
Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation
Amanda Resende, Davis Railsback, Rafael Dowsley +2
We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while anot…
High Performance Logistic Regression for Privacy-Preserving Genome Analysis
Martine De Cock, Rafael Dowsley, Anderson C. A. Nascimento +3
In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of…