Abstract
The paper presents a method for the privacy-preserving learning of random forests from private data of three parties, where not even the decision trees, i.e., neither the tree structures nor their parameters (the annotations of attributes and attribute values), are disclosed to any of the parties. To make this practical for realistically size data, a custom protocol is needed for the private comparison of two numbers, such that the numbers themselves are only available in shares and are not known to either party. Experiments with five datasets indicate that the overall protocol matches classical random forests in accuracy and can handle datasets of realistic size.
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Notes
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Notice that we follow the original definition of random forests by Breiman (2001).
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Acknowledgements
This work was partly funded by the Carl-Zeiss-Stiftung as part of the CZS Durchbrueche project under grant number [P2021-02-014].
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Bammert, LM., Kramer, S., Cerrato, M., Althaus, E. (2023). Privacy-Preserving Learning of Random Forests Without Revealing the Trees. In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham. https://doi.org/10.1007/978-3-031-45275-8_25
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