Privacy-preserving machine learning for healthcare: open challenges and
future perspectives
- URL: http://arxiv.org/abs/2303.15563v1
- Date: Mon, 27 Mar 2023 19:20:51 GMT
- Title: Privacy-preserving machine learning for healthcare: open challenges and
future perspectives
- Authors: Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail
Maniatakos, Farah E. Shamout
- Abstract summary: 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.
The aim of this review is to guide the development of private and efficient ML models in healthcare.
- Score: 72.43506759789861
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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