MedCo2: Privacy-Preserving Cohort Exploration and Analysis.

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Version: Final published version
License: CC BY-NC 4.0
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serval:BIB_D0C4C9C55DD4
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Title
MedCo2: Privacy-Preserving Cohort Exploration and Analysis.
Title of the book
Digital Personalized Health and Medicine
Author(s)
Froelicher David, Mickaël Misbach, Troncoso-Pastoriza Juan Ramon, Raisaro Jean Louis, Hubaux Jean-Pierre
Publisher
IOS Press
Publication state
Published
Issued date
01/06/2020
Volume
270
Series
Studies in health technology and informatics
Pages
317-321
Language
english
Abstract
Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients is difficult and usually requires an international-scale research. In this case, the process can be even more difficult due to the heterogeneity of data-protection regulations, making the data sharing process particularly hard. In this short paper, we propose MedCo2 (pronounced MedCo square), a distributed system that streamlines the process of a medical study by bridging and enabling both data discovery and data analysis among multiple databases, while protecting data confidentiality and patients' privacy. MedCo2 relies on interactive protocols, homomorphic encryption and differential privacy. It enables the privacy-preserving computations of multiple statistics such as cosine similarity and variance, and the training of machine learning models, on patients that are obliviously selected according to specific criteria among multiple databases.
Pubmed
Create date
15/11/2022 11:26
Last modification date
05/09/2024 9:12
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