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The academic community has proposed many solutions to address the privacy concerns associated with genomic-data sharing. However, practitioners have not adopted these solutions due to their impact on the data utility. To address this problem, we introduce GenoShare, a framework that helps practitioners to make informed decisions about the sharing of exact genomic data by providing means to systematically reason about the risk of disclosing privacy-sensitive attributes (e.g., health status, kinship, physical traits). We instantiate GenoShare with three of the most important genomics-oriented inference attacks, and demonstrate its capability to detect potential leakage of sensitive attributes using real data from the 1000 Genomes Project.
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Stewart Cole, Andrej Benjak, Charlotte Avanzi, Philippe Busso, Pushpendra Singh, Thyago Leal Calvo