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arXiv Machine Learning By Dani\"el Vos, Jelle Vos, Tianyu Li, Zekeriya Erkin, Sicco Verwer

Differentially-Private Decision Trees and Provable Robustness to Data Poisoning

Read the original on arXiv Machine Learning →

The paper introduces PrivaTree, a differentially‑private decision tree algorithm that uses private histograms to select splits while preserving a small privacy budget. PrivaTree supports mixed numerical and categorical data without leaking information about numerical features and achieves a superior privacy‑utility trade‑off compared to existing methods. Additionally, the authors provide theoretical bounds on the expected accuracy and success rates of backdoor attacks, showing that PrivaTree-trained trees are more robust against data poisoning than standard decision trees.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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