Collaborative Privacy-Preserving Training of Decision Trees

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dc.contributor.authorEl-Zein, Yamane
dc.contributor.authorDelgado-Gonzalo, Ricard
dc.contributor.authorHuguenin, Kevin
dc.date.accessioned2025-11-11T15:37:36Z
dc.date.available2025-11-11T15:37:36Z
dc.date.issued2021
dc.description.abstractAs 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.
dc.identifier.citationCSEM Scientific and Technical Report 2021, p. 43
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1770
dc.titleCollaborative Privacy-Preserving Training of Decision Trees
dc.typeCSEM Report
dc.type.csemdivisionsBU-D
dc.type.csemresearchareasIoT & Vision
dc.type.csemresearchareasDigital Health
dc.type.csemresearchareasData & AI
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