Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge.

TitleScalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge.
Publication TypeJournal Article
Year of Publication2014
AuthorsYu, F, Ji, Z
JournalBMC Med Inform Decis Mak
Volume14 Suppl 1
PaginationS3
Date Published2014 Dec 8
ISSN1472-6947
iDASH CategoryPrivacy Technology
Abstract<p>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 χ2 statistic in a GWAS setting and new results about the Hamming distance score, a key component for one of the privacy-preserving methods.</p>
DOI10.1186/1472-6947-14-S1-S3
Alternate JournalBMC Med Inform Decis Mak
PubMed ID25521367
PubMed Central IDPMC4290802
Grant ListK99HG008175 / HG / NHGRI NIH HHS / United States
R00LM011392 / LM / NLM NIH HHS / United States
R01 HG007078 / HG / NHGRI NIH HHS / United States
R01HG007078 / HG / NHGRI NIH HHS / United States
R21LM012060 / LM / NLM NIH HHS / United States
U54HL108460 / HL / NHLBI NIH HHS / United States
UL1TR000100 / TR / NCATS NIH HHS / United States