TY - GEN
T1 - Privacy-Preserving Machine Learning for Healthcare
T2 - Trustworthy Machine Learning for Healthcare - First International Workshop, TML4H 2023, Proceedings
AU - Guerra-Manzanares, Alejandro
AU - Lopez, L. Julian Lechuga
AU - Maniatakos, Michail
AU - Shamout, Farah E.
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - healthcare
KW - machine learning
KW - privacy-preserving
UR - http://www.scopus.com/inward/record.url?scp=85172251507&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85172251507&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-39539-0_3
DO - 10.1007/978-3-031-39539-0_3
M3 - Conference contribution
AN - SCOPUS:85172251507
SN - 9783031395383
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 25
EP - 40
BT - Trustworthy Machine Learning for Healthcare - 1st International Workshop, TML4H 2023, Proceedings
A2 - Chen, Hao
A2 - Luo, Luyang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 May 2023 through 4 May 2023
ER -