2013 PrivacyPreservingDataExploratio

• (Johnson & Shmatikov, 2013) ⇒ Aaron Johnson, and Vitaly Shmatikov. (2013). “Privacy-preserving Data Exploration in Genome-wide Association Studies.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487687

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Cited By

• http://scholar.google.com/scholar?q=%222013%22+Privacy-preserving+Data+Exploration+in+Genome-wide+Association+Studies
• http://dl.acm.org/citation.cfm?id=2487575.2487687&preflayout=flat#citedby

Quotes

Author Keywords

• Biology and genetics; data mining; differential privacy; genome-wide association studies; health

Abstract

Genome-wide association studies (GWAS) have become a popular method for analyzing sets of DNA sequences in order to discover the genetic basis of disease. Unfortunately, statistics published as the result of GWAS can be used to identify individuals participating in the study. To prevent privacy breaches, even previously published results have been removed from public databases, impeding researchers' access to the data and hindering collaborative research. Existing techniques for privacy-preserving GWAS focus on answering specific questions, such as correlations between a given pair of SNPs (DNA sequence variations). This does not fit the typical GWAS process, where the analyst may not know in advance which SNPs to consider and which statistical tests to use, how many SNPs are significant for a given dataset, etc. We present a set of practical, privacy-preserving data mining algorithms for GWAS datasets. Our framework supports exploratory data analysis, where the analyst does not know a priori how many and which SNPs to consider. We develop privacy-preserving algorithms for computing the number and location of SNPs that are significantly associated with the disease, the significance of any statistical test between a given SNP and the disease, any measure of correlation between SNPs, and the block structure of correlations. We evaluate our algorithms on real-world datasets and demonstrate that they produce significantly more accurate results than prior techniques while guaranteeing differential privacy.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 PrivacyPreservingDataExploratio	Vitaly Shmatikov Aaron Johnson			Privacy-preserving Data Exploration in Genome-wide Association Studies				10.1145/2487575.2487687		2013