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Trained Random Forests Completely Reveal your Dataset
March 1, 2024, 5:43 a.m. | Julien Ferry, Ricardo Fukasawa, Timoth\'ee Pascal, Thibaut Vidal
cs.LG updates on arXiv.org arxiv.org
Abstract: We introduce an optimization-based reconstruction attack capable of completely or near-completely reconstructing a dataset utilized for training a random forest. Notably, our approach relies solely on information readily available in commonly used libraries such as scikit-learn. To achieve this, we formulate the reconstruction problem as a combinatorial problem under a maximum likelihood objective. We demonstrate that this problem is NP-hard, though solvable at scale using constraint programming -- an approach rooted in constraint propagation and …
abstract arxiv cs.cr cs.lg dataset forests information learn libraries near optimization random random forests scikit scikit-learn training type
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