Primed: Private Federated Training and Encrypted Inference on Medical Images in Healthcare

Gopalakrishnan, Aparna; Kulkarni, Narayan P.; Raghavendra, Chethan B.; Manjappa, Raghavendra; Honnavalli, Prasad B; Eswaran, Sivaraman

doi:10.2139/ssrn.4196696

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Additionally, some work only evaluate their solutions on computer vision benchmark datasets (e.g., MNIST or CIFAR-10) inferring that good performance on these datasets will provide similar results on medical image data (Festag & Spreckelsen, 2020;Onesimu & Karthikeyan, 2020). However, this assumption is not empirically supported by work that uses both medical and non-medical datasets (Suriyakumar et al, 2021;Gopalakrishnan et al, 2021;Jarin & Eshete, 2021;Vizitiu et al, 2020) and must, therefore, be avoided.…”

Section: Open Challengesmentioning

confidence: 99%

Privacy-preserving machine learning for healthcare: open challenges and future perspectives

Guerra-Manzanares¹,

Lopez²,

Maniatakos³

et al. 2023

Preprint

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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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