16 citations · 51 across the 10 of their papers we have counts for
15 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…
Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines
Rafael Dowsley, Caleb Horst, Anderson C. A. Nascimento
We propose protocols for obliviously evaluating finite-state machines, i.e., the evaluation is shared between the provider of the finite-state machine and the provider of the input…
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…
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection
Devin Reich, Ariel Todoki, Rafael Dowsley +2
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal tex…