Scientific article
English

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

Publication date2019
First online date2018-07-13
Abstract

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.

Keywords
  • Algorithms
  • Computer Security
  • Confidentiality
  • Electronic Health Records
  • Genome, Human
  • Genomics
  • Hospitals
  • Humans
  • Internet
  • Medical Informatics / methods
  • Mutation
  • Neoplasms / genetics
  • Proto-Oncogene Proteins B-raf / genetics
  • Software
Affiliation entities Not a UNIGE publication
Citation (ISO format)
RAISARO, Jean Louis et al. MedCo : Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data. In: IEEE/ACM transactions on computational biology and bioinformatics, 2019, vol. 16, n° 4, p. 1328–1341. doi: 10.1109/TCBB.2018.2854776
Main files (1)
Article (Published version)
accessLevelRestricted
Identifiers
Additional URL for this publicationhttps://ieeexplore.ieee.org/document/8410926
Journal ISSN1545-5963
4views
0downloads

Technical informations

Creation28/11/2025 17:30:32
First validation16/02/2026 08:26:28
Update16/02/2026 08:26:28
Status update16/02/2026 08:26:28
Last indexation16/02/2026 08:26:29
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack