Collaborative Privacy-Preserving Training of Decision Trees
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Author
El-Zein, Yamane
Delgado-Gonzalo, Ricard
Huguenin, Kevin
DOI
Abstract
As data generation becomes more ubiquitous, datasets are increasingly spread across several entities. Exploiting such distributed datasets simultaneously, by training machine learning models on them, can be beneficial to many fields, but also raises serious privacy concerns. In this work, a scalable protocol is designed, implemented, and evaluated, for training decision tree models collaboratively across mutually distrustful data providers, while ensuring privacy of each of the stakeholders' data.
Publication Reference
CSEM Scientific and Technical Report 2021, p. 43
Year
2021