Abstract
The decision tree is a typical algorithm in machine learning and has multiple expanded variations. However, regarding privacy, few in the variations reached practical level due to many challenges on balancing privacy preservation and performance. In this paper, we propose a method of applying privacy preservation to the (random) decision tree, which is a variation of the expanded decision tree proposed by Fan et al. in 2003, to achieve the following goals:
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Model training with data belonging to multiple organizations and concealing these data among organizations.
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No leakage of training data from trained models.
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Notes
- 1.
 We use the Pedersen commitment and the expanded ElGamal encryption (plaintext m is encoded as \(g^m\)).
- 2.
 (f-out-of-M)-threshold decryption means any f out of M participants cooperate can decrypt the ciphertexts, but any participants less than f cannot.
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Acknowledgements
This work was supported in part by JST CREST Grant Number JPMJCR21M1, and JSPS KAKENHI Grant Number JP20K11826, Japan.
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Nojima, R., Wang, L. (2024). Differential Private (Random) Decision Tree Without Adding Noise. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1963. Springer, Singapore. https://doi.org/10.1007/978-981-99-8138-0_14
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