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Citizen-centered, auditable and privacy-preserving population genomics.

Dennis GrishinJean Louis RaisaroJuan Ramón Troncoso-PastorizaKamal ObbadKevin QuinnMickaël MisbachJared GollhardtJoao SaJacques FellayGeorge M ChurchJean-Pierre Hubaux
Published in: Nature computational science (2021)
The growing number of health-data breaches, the use of genomic databases for law enforcement purposes and the lack of transparency of personal genomics companies are raising unprecedented privacy concerns. To enable a secure exploration of genomic datasets with controlled and transparent data access, we propose a citizen-centric approach that combines cryptographic privacy-preserving technologies, such as homomorphic encryption and secure multi-party computation, with the auditability of blockchains. Our open-source implementation supports queries on the encrypted genomic data of hundreds of thousands of individuals, with minimal overhead. We show that real-world adoption of our system alleviates widespread privacy concerns and encourages data access sharing with researchers.
Keyphrases
  • big data
  • health information
  • electronic health record
  • artificial intelligence
  • healthcare
  • machine learning
  • public health
  • copy number
  • mental health
  • single cell
  • risk assessment
  • human health
  • genome wide