Privacy-Preserving Decision Trees Training and Prediction

Adi Akavia, Max Leibovich, Yehezkel S. Resheff, Roey Ron, Moni Shahar, Margarita Vald

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the era of cloud computing and machine learning, data has become a highly valuable resource. Recent history has shown that the benefits brought forth by this data driven culture come at a cost of potential data leakage. Such breaches have a devastating impact on individuals and industry, and lead the community to seek privacy preserving solutions. A promising approach is to utilize Fully Homomorphic Encryption (FHE ) to enable machine learning over encrypted data, thus providing resiliency against information leakage. However, computing over encrypted data incurs a high computational overhead, thus requiring the redesign of algorithms, in an “ FHE -friendly” manner, to maintain their practicality. In this work we focus on the ever-popular tree based methods (e.g., boosting, random forests), and propose a new privacy-preserving solution to training and prediction for trees. Our solution employs a low-degree approximation for the step-function together with a lightweight interactive protocol, to replace components of the vanilla algorithm that are costly over encrypted data. Our protocols for decision trees achieve practical usability demonstrated on standard UCI datasets, encrypted with fully homomorphic encryption. In addition, the communication complexity of our protocols is independent of the tree size and dataset size in prediction and training, respectively, which significantly improves on prior works.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings
EditorsFrank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
PublisherSpringer Science and Business Media Deutschland GmbH
Pages145-161
Number of pages17
ISBN (Print)9783030676575
DOIs
StatePublished - 2021
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duration: 14 Sep 202018 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12457 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online
Period14/09/2018/09/20

Bibliographical note

Funding Information:
Keywords: Fully homomorphic encryption · Privacy preserving machine learning · Decision trees · Training · Prediction The first author thanks the Israel Science Foundation (grant 3380/19) and Israel National Cyber Directorate via the Haifa, BIU and Tel-Aviv cyber centers for their support. The authors wish to thank Yaron Sheffer for helpful discussions.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Decision trees
  • Fully homomorphic encryption
  • Prediction
  • Privacy preserving machine learning
  • Training

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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