A cryptography game-changer for biomedical research at scale

Date:
October 11, 2021
Source:
Ecole Polytechnique Fe'de'rale de Lausanne
Summary:
Using cutting-edge cryptographic techniques (multiparty homomorphic
encryption), a new platform called FAMHE will act as a game-changer
towards precision, personalized medicine.



FULL STORY ========================================================================== Predictive, preventive, personalized and participatory medicine, known as
P4, is the healthcare of the future. To both accelerate its adoption and maximize its potential, clinical data on large numbers of individuals
must be efficiently shared between all stakeholders. However, data is
hard to gather.

It's siloed in individual hospitals, medical practices, and clinics
around the world. Privacy risks stemming from disclosing medical data
are also a serious concern, and without effective privacy preserving technologies, have become a barrier to advancing P4 medicine.


========================================================================== Existing approaches either provide only limited protection of patients'
privacy by requiring the institutions to share intermediate results,
which can in turn leak sensitive patient-level information, or they
sacrifice the accuracy of results by adding noise to the data to mitigate potential leakage.

Now, researchers from EPFL's Laboratory for Data Security, working with colleagues at Lausanne University Hospital (CHUV), MIT CSAIL, and the
Broad Institute of MIT and Harvard, have developed "FAMHE." This federated analytics system enables different healthcare providers to collaboratively perform statistical analyses and develop machine learning models, all
without exchanging the underlying datasets. FAHME hits the sweet spot
between data protection, accuracy of research results, and practical computational time - - three critical dimensions in the biomedical
research field.

In a paper published in Nature Communications on October 11, the research
team says the crucial difference between FAMHE and other approaches
trying to overcome the privacy and accuracy challenges is that FAMHE
works at scale and it has been mathematically proven to be secure,
which is a must due to the sensitivity of the data.

In two prototypical deployments, FAMHE accurately and efficiently
reproduced two published, multi-centric studies that relied on data centralization and bespoke legal contracts for data transfer centralized studies -- including Kaplan-Meier survival analysis in oncology and
genome-wide association studies in medical genetics. In other words,
they have shown that the same scientific results could have been achieved
even if the the datasets had not been transferred and centralized.

"Until now, no one has been able to reproduce studies that show that
federated analytics works at scale. Our results are accurate and are
obtained with a reasonable computation time. FAMHE uses multiparty
homomorphic encryption, which is the ability to make computations on the
data in its encrypted form across different sources without centralizing
the data and without any party seeing the other parties' data" says EPFL Professor Jean-Pierre Hubaux, the study's lead senior author.

"This technology will not only revolutionize multi-site clinical research studies, but also enable and empower collaborations around sensitive
data in many different fields such as insurance, financial services
and cyberdefense, among others," adds EPFL senior researcher Dr. Juan Troncoso-Pastoriza.

Patient data privacy is a key concern of the Lausanne University Hospital.

"Most patients are keen to share their health data for the advancement of science and medicine, but it is essential to ensure the confidentiality
of such sensitive information. FAMHE makes it possible to perform secure collaborative research on patient data at an unprecedented scale,"
says Professor Jacques Fellay from CHUV Precision Medicine unit.

"This is a game-changer towards personalized medicine, because, as long
as this kind of solution does not exist, the alternative is to set up
bilateral data transfer and use agreements, but these are ad hoc and
they take months of discussion to make sure the data is going to be
properly protected when this happens. FAHME provides a solution that
makes it possible once and for all to agree on the toolbox to be used
and then deploy it," says Prof. Bonnie Berger of MIT, CSAIL, and Broad.

"This work lays down a key foundation on which federated learning
algorithms for a range of biomedical studies could be built in a scalable manner. It is exciting to think about possible future developments of
tools and workflows enabled by this system to support diverse analytic
needs in biomedicine," says Dr. Hyunghoon Cho at the Broad Institute.

So how fast and how far do the researchers expect this new solution
to spread? "We are in advanced discussions with partners in Texas,
The Netherlands, and Italy to deploy FAMHE at scale. We want this to
become integrated in routine operations for medical research," says CHUV
Dr. Jean Louis Raisaro, one of the senior investigators of the study.

========================================================================== Story Source: Materials provided by
Ecole_Polytechnique_Fe'de'rale_de_Lausanne. Original written by Tanya
Petersen. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. David Froelicher, Juan R. Troncoso-Pastoriza, Jean Louis Raisaro,
Michel
A. Cuendet, Joao Sa Sousa, Hyunghoon Cho, Bonnie Berger,
Jacques Fellay, Jean-Pierre Hubaux. Truly privacy-preserving
federated analytics for precision medicine with multiparty
homomorphic encryption. Nature Communications, 2021; 12 (1) DOI:
10.1038/s41467-021-25972-y ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/10/211011091301.htm

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