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MedCo(2): Privacy-Preserving Cohort Exploration and Analysis

David Jules Froelicher, Jean-Pierre Hubaux, Mickaël Misbach, Jean Louis Raisaro, Juan Ramón Troncoso-Pastoriza

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 MedCo(2) (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. MedCo(2) 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.

IOS PRESS2020

MedCo: Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data

Bryan Alexander Ford, Joao André Gomes de Sá E Sousa, Jean-Pierre Hubaux, Olivier Michielin, Mickaël Misbach, Jean Louis Raisaro, Juan Ramón Troncoso-Pastoriza

The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, for medical progress. Therefore, the development of new solutions that can reassure clinical sites by enabling privacy-preserving sharing of sensitive medical data in compliance with stringent regulations (e.g., HIPAA, GDPR) is now more urgent than ever. In this work, we introduce MedCo, the first operational system that enables a group of clinical sites to federate and collectively protect their data in order to share them with external investigators without worrying about security and privacy concerns. MedCo uses (a) collective homomorphic encryption to provide trust decentralization and end-to-end confidentiality protection, and (b) obfuscation techniques to achieve formal notions of privacy, such as differential privacy. A critical feature of MedCo is that it is fully integrated within the i2b2 (Informatics for Integrating Biology and the Bedside) framework, currently used in more than 300 hospitals worldwide. Therefore, it is easily adoptable by clinical sites. We demonstrate MedCo’s practicality by testing it on data from The Cancer Genome Atlas in a simulated network of three institutions. Its performance is comparable to the ones of SHRINE (networked i2b2), which, in contrast, does not provide any data protection guarantee.

2019

Building a common and privacy-preserving front end for open-source clinical research platforms

Mickaël Misbach

Being able to exploit large and heterogeneous medical data is crucial for realizing the promise of precision medicine to its full potential. Yet, currently, due to the presence of multiple and fragmented systems at different clinical sites, it is often difficult to enable researchers to access the data they need. Privacy and security concerns also represent major obstacles that need to be overcome in order to provide access to sensitive medical data that are usually not exposed by clinical sites for the fear of data leaks. To address these challenges, we propose a system that employs Glowing Bear as a common front end to the widespread clinical research platforms i2b2, tranSMART and MedCo. Our proposed system uses IRCT, the official implementation of the PIC-SURE API, which acts as an interoperability layer that translates clinical research queries into native API languages. With our work, we take a step towards the technical convergence of i2b2 and tranSMART, giving clinical sites the means to share their data more easily. Even more, with the support of MedCo, a cohort explorer with strong privacy and security guarantees based on federated i2b2 instances, we enable clinical sites to share sensitive data that would be difficult to share otherwise.

2018