Privacy Enhanced Decision Tree Inference
Abstract
In many areas in machine learning, decision trees play a crucial role in classification and regression. When a decision tree based classifier is hosted as a service in a critical application with the need for privacy protection of the service as well as the user data, fully homomorphic encrypted can be employed. However, a decision node in a decision tree can’t be directly implemented in FHE. In this paper, we describe an end-to-end approach to support privacyenhanced decision tree classification using IBM supported open-source library HELib. Using several options for building a decision node and employing oblivious computations coupled with an argmax function in FHE we show that a highly secure and trusted decision tree service can be enabled.
Cite
Text
Sarpatwar et al. "Privacy Enhanced Decision Tree Inference." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00025Markdown
[Sarpatwar et al. "Privacy Enhanced Decision Tree Inference." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/sarpatwar2020cvprw-privacy/) doi:10.1109/CVPRW50498.2020.00025BibTeX
@inproceedings{sarpatwar2020cvprw-privacy,
title = {{Privacy Enhanced Decision Tree Inference}},
author = {Sarpatwar, Kanthi K. and Ratha, Nalini K. and Nandakumar, Karthik and Shanmugam, Karthikeyan and Rayfield, James T. and Pankanti, Sharath and Vaculín, Roman},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {154-159},
doi = {10.1109/CVPRW50498.2020.00025},
url = {https://mlanthology.org/cvprw/2020/sarpatwar2020cvprw-privacy/}
}