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

    Keywords

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

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