Research#Privacy-Preserving ML#Random Forests#Secure Computation
Privacy-preserving training of tree ensembles over continuous data
Overview of our work on secure multi-party computation techniques for training decision trees and random forests while preserving privacy.
Authors: S Adams, C Choudhary, Martine De Cock, Rafael Dowsley, D Melanson, Jianwei Shen, et al.
Published: 2022-02-10
Abstract
This paper presents novel protocols for privacy-preserving training of decision trees and random forests over continuous data using secure multi-party computation techniques.
Citation
Adams, S., Choudhary, C., De Cock, M., Dowsley, R., Melanson, D., Shen, J., et al. (2022). Privacy-preserving training of tree ensembles over continuous data. Proceedings on Privacy Enhancing Technologies, 2022(2), 205-226.
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