Literature DB >> 32570382

GenoShare: Supporting Privacy-Informed Decisions for Sharing Individual-Level Genetic Data.

Jean Louis Raisaro1, Juan Ramón Troncoso-Pastoriza2, Yamane El-Zein3, Mathias Humbert4, Carmela Troncoso2, Jacques Fellay1, Jean-Pierre Hubaux2.   

Abstract

One major obstacle to developing precision medicine to its full potential is the privacy concerns related to genomic-data sharing. Even though the academic community has proposed many solutions to protect genomic privacy, these so far have not been adopted in practice, mainly due to their impact on the data utility. We introduce GenoShare, a framework that enables individual citizens to understand and quantify the risks of revealing genome-related privacy-sensitive attributes (e.g., health status, kinship, physical traits) from sharing their genomic data with (potentially untrusted) third parties. GenoShare enables informed decision-making about sharing exact genomic data, by jointly simulating genome-based inference attacks and quantifying the risk stemming from a potential data disclosure.

Keywords:  genomic privacy; inference; privacy-conscious tools; risk quantification

Mesh:

Year:  2020        PMID: 32570382     DOI: 10.3233/SHTI200158

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  1 in total

1.  SVAT: Secure outsourcing of variant annotation and genotype aggregation.

Authors:  Miran Kim; Su Wang; Xiaoqian Jiang; Arif Harmanci
Journal:  BMC Bioinformatics       Date:  2022-10-01       Impact factor: 3.307

  1 in total

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