Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives

Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos, Farah E. Shamout

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.

Original languageEnglish (US)
Title of host publicationTrustworthy Machine Learning for Healthcare - 1st International Workshop, TML4H 2023, Proceedings
EditorsHao Chen, Luyang Luo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages25-40
Number of pages16
ISBN (Print)9783031395383
DOIs
StatePublished - 2023
EventTrustworthy Machine Learning for Healthcare - First International Workshop, TML4H 2023, Proceedings - Virtual, Online
Duration: May 4 2023May 4 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13932 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceTrustworthy Machine Learning for Healthcare - First International Workshop, TML4H 2023, Proceedings
CityVirtual, Online
Period5/4/235/4/23

Keywords

  • healthcare
  • machine learning
  • privacy-preserving

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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