A Distributed and Privacy-Preserving Random Forest Evaluation Scheme with Fine Grained Access Control
Random forest is a simple and effective model for ensemble learning with wide potential applications. Implementation of random forest evaluations while preserving privacy for the source data is demanding but also challenging. In this paper, we propose a practical and fault-tolerant privacy-preservin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/14/2/415 |