5 citations · 9 across the 2 of their papers we have counts for
3 papers
PrivFair: a Library for Privacy-Preserving Fairness Auditing
Sikha Pentyala, David Melanson, Martine De Cock +1
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance. ML models have been found t…
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…