Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challengeReportar como inadecuado




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BMC Medical Informatics and Decision Making

, 14:S3

First Online: 08 December 2014

Abstract

In response to the growing interest in genome-wide association study GWAS data privacy, the Integrating Data for Analysis, Anonymization and SHaring iDASH center organized the iDASH Healthcare Privacy Protection Challenge, with the aim of investigating the effectiveness of applying privacy-preserving methodologies to human genetic data. This paper is based on a submission to the iDASH Healthcare Privacy Protection Challenge. We apply privacy-preserving methods that are adapted from Uhler et al. 2013 and Yu et al. 2014 to the challenge-s data and analyze the data utility after the data are perturbed by the privacy-preserving methods. Major contributions of this paper include new interpretation of the χ statistic in a GWAS setting and new results about the Hamming distance score, a key component for one of the privacy-preserving methods.

Keywordsχ statistic Contingency table Differential privacy Genome-wide association study GWAS Data sharing Single-nucleotide polymorphism Electronic supplementary materialThe online version of this article doi:10.1186-1472-6947-14-S1-S3 contains supplementary material, which is available to authorized users.

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Autor: Fei Yu - Zhanglong Ji

Fuente: https://link.springer.com/







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