Privacy Enhanced Decision Tree Inference

Kanthi Sarpatwar, Nalini K. Ratha, Karthik Nandakumar, Karthikeyan Shanmugam, James T. Rayfield, Sharath Pankanti, Roman Vaculin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 34-35

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 privacy-enhanced 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.

Related Material

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[bibtex]
@InProceedings{Sarpatwar_2020_CVPR_Workshops,
author = {Sarpatwar, Kanthi and Ratha, Nalini K. and Nandakumar, Karthik and Shanmugam, Karthikeyan and Rayfield, James T. and Pankanti, Sharath and Vaculin, Roman},
title = {Privacy Enhanced Decision Tree Inference},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}