Format

Send to

Choose Destination
Am J Hum Genet. 2017 Feb 2;100(2):316-322. doi: 10.1016/j.ajhg.2016.12.002. Epub 2017 Jan 5.

Expanding Access to Large-Scale Genomic Data While Promoting Privacy: A Game Theoretic Approach.

Author information

1
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA. Electronic address: zhiyu.wan@vanderbilt.edu.
2
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA.
3
Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN 37203, USA; Law School, Vanderbilt University, Nashville, TN 37203, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
4
Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA.
5
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA. Electronic address: b.malin@vanderbilt.edu.

Abstract

Emerging scientific endeavors are creating big data repositories of data from millions of individuals. Sharing data in a privacy-respecting manner could lead to important discoveries, but high-profile demonstrations show that links between de-identified genomic data and named persons can sometimes be reestablished. Such re-identification attacks have focused on worst-case scenarios and spurred the adoption of data-sharing practices that unnecessarily impede research. To mitigate concerns, organizations have traditionally relied upon legal deterrents, like data use agreements, and are considering suppressing or adding noise to genomic variants. In this report, we use a game theoretic lens to develop more effective, quantifiable protections for genomic data sharing. This is a fundamentally different approach because it accounts for adversarial behavior and capabilities and tailors protections to anticipated recipients with reasonable resources, not adversaries with unlimited means. We demonstrate this approach via a new public resource with genomic summary data from over 8,000 individuals-the Sequence and Phenotype Integration Exchange (SPHINX)-and show that risks can be balanced against utility more effectively than with traditional approaches. We further show the generalizability of this framework by applying it to other genomic data collection and sharing endeavors. Recognizing that such models are dependent on a variety of parameters, we perform extensive sensitivity analyses to show that our findings are robust to their fluctuations.

KEYWORDS:

Electronic Medical Records and Genomics Network; Sequence and Phenotype Integration Exchange; adversarial modeling; game theory; genetic algorithm; genomic data privacy; genomic data sharing policy; re-identification risk; sensitivity analysis; summary statistics

PMID:
28065469
PMCID:
PMC5294764
DOI:
10.1016/j.ajhg.2016.12.002
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center