Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.
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| Title: | Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. |
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| Authors: | Froelicher D; Laboratory for Data Security, EPFL, Lausanne, Switzerland., Troncoso-Pastoriza JR; Laboratory for Data Security, EPFL, Lausanne, Switzerland., Raisaro JL; Precision Medicine Unit, Lausanne University Hospital, Lausanne, Switzerland.; Data Science Group, Lausanne University Hospital, Lausanne, Switzerland., Cuendet MA; Precision Oncology Center, Lausanne University Hospital, Lausanne, Switzerland., Sousa JS; Laboratory for Data Security, EPFL, Lausanne, Switzerland., Cho H; Broad Institute of MIT and Harvard, Cambridge, MA, USA., Berger B; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Computer Science and AI Laboratory, MIT, Cambridge, MA, USA.; Department of Mathematics, MIT, Cambridge, MA, USA., Fellay J; Precision Medicine Unit, Lausanne University Hospital, Lausanne, Switzerland.; School of Life Sciences, EPFL, Lausanne, Switzerland., Hubaux JP; Laboratory for Data Security, EPFL, Lausanne, Switzerland. jean-pierre.hubaux@epfl.ch. |
| Source: | Nature communications [Nat Commun] 2021 Oct 11; Vol. 12 (1), pp. 5910. Date of Electronic Publication: 2021 Oct 11. |
| Publication Type: | Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2041-1723 |
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| DOI: | 10.1038/s41467-021-25972-y |
