Privacy-Preserving Scoring of Tree Ensembles: A Novel Framework for AI in Healthcare

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Kyle Fritchman, University of Washington Tacoma
  • ,
  • Keerthanaa Saminathan, University of Washington Tacoma
  • ,
  • Rafael Dowsley
  • ,
  • Tyler Hughes, KenSci
  • ,
  • Martine De Cock, University of Washington Tacoma, Universiteit Gent
  • ,
  • Anderson Nascimento, University of Washington Tacoma
  • ,
  • Ankur Teredesai, University of Washington Tacoma, KenSci

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
Number of pages10
Publication year2019
Article number8622627
ISBN (Electronic)9781538650356
Publication statusPublished - 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: 10 Dec 201813 Dec 2018


Conference2018 IEEE International Conference on Big Data, Big Data 2018
LandUnited States
SponsorBaidu, et al, Expedia Group, IEEE, IEEE Computer Society, Squirrel AI Learning

    Research areas

  • boosted decision trees, encryption, healthcare, privacy-preserving machine learning, random forest, secure multiparty computation

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