Systematic review of privacy-preserving distributed machine learning from federated databases in health care
Big data for health care is one of the potential solutions to deal with the numerous
challenges of health care, such as rising cost, aging population, precision medicine …
challenges of health care, such as rising cost, aging population, precision medicine …
Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States
Accessing and integrating human genomic data with phenotypes are important for
biomedical research. Making genomic data accessible for research purposes, however …
biomedical research. Making genomic data accessible for research purposes, however …
Modelchain: Decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks
TT Kuo, L Ohno-Machado - arXiv preprint arXiv:1802.01746, 2018 - arxiv.org
Cross-institutional healthcare predictive modeling can accelerate research and facilitate
quality improvement initiatives, and thus is important for national healthcare delivery …
quality improvement initiatives, and thus is important for national healthcare delivery …
[HTML][HTML] Secure logistic regression based on homomorphic encryption: Design and evaluation
Background: Learning a model without accessing raw data has been an intriguing idea to
security and machine learning researchers for years. In an ideal setting, we want to encrypt …
security and machine learning researchers for years. In an ideal setting, we want to encrypt …
Scalable and secure logistic regression via homomorphic encryption
Y Aono, T Hayashi, L Trieu Phong, L Wang - Proceedings of the sixth …, 2016 - dl.acm.org
Logistic regression is a powerful machine learning tool to classify data. When dealing with
sensitive data such as private or medical information, cares are necessary. In this paper, we …
sensitive data such as private or medical information, cares are necessary. In this paper, we …
[HTML][HTML] Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT
Abstract Machine learning applications for personalized medicine are highly dependent on
access to sufficient data. For personalized radiation oncology, datasets representing the …
access to sufficient data. For personalized radiation oncology, datasets representing the …
Privacy-preserving logistic regression with distributed data sources via homomorphic encryption
Logistic regression is a powerful machine learning tool to classify data. When dealing with
sensitive or private data, cares are necessary. In this paper, we propose a secure system for …
sensitive or private data, cares are necessary. In this paper, we propose a secure system for …
[HTML][HTML] Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer
Introduction Emerging evidence suggests that data-driven support tools have found their
way into clinical decision-making in a number of areas, including cancer care. Improving …
way into clinical decision-making in a number of areas, including cancer care. Improving …
EX pectation P ropagation LO gistic RE g R ession on permissioned block CHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model …
TT Kuo, RA Gabriel, KR Cidambi… - Journal of the …, 2020 - academic.oup.com
Objective Predicting patient outcomes using healthcare/genomics data is an increasingly
popular/important area. However, some diseases are rare and require data from multiple …
popular/important area. However, some diseases are rare and require data from multiple …
Privacy-preserving GWAS analysis on federated genomic datasets
Background The biomedical community benefits from the increasing availability of genomic
data to support meaningful scientific research, eg, Genome-Wide Association Studies …
data to support meaningful scientific research, eg, Genome-Wide Association Studies …