Welcome to the resource topic for 2025/1409
Title:
Oblivious (Un)Learning of Extremely Randomized Trees
Authors: Sofiane Azogagh, Zelma Aubin Birba, Sébastien Gambs, Marc-Olivier Killijian
Abstract:While the use of homomorphic encryption (HE) for encrypted inference has received considerable attention, its application for the training of machine learning (ML) models remains comparatively underexplored, primarily due to the high computational overhead traditionally associated with fully homomorphic encryption (FHE).
In this work, we address this challenge by leveraging the inherent connection between inference and training in the context of Extremely Randomized Trees (ERT), thereby enabling efficient training directly over encrypted data.
More precisely, we instantiate this approach by the training of ERT within the TFHE framework.
Our implementation demonstrates that it is possible to train ERTs on encrypted datasets with a runtime significantly lower than current state-of-the-art methods for training Random Forests in the encrypted domain while achieving comparable predictive accuracy.
This result highlights a promising direction for practical privacy-preserving machine learning using FHE.
Our second main contribution consists in leveraging the properties of ERTs to create the first ML model that enables private unlearning. This approach makes the unlearning process indistinguishable from training, thus allowing clients to conceal the true nature of the operations being conducted on the model.
ePrint: https://eprint.iacr.org/2025/1409
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