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

Overview of attention for article published in American Journal of Human Genetics, January 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#45 of 5,881)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
33 news outlets
twitter
46 X users
facebook
2 Facebook pages
wikipedia
3 Wikipedia pages
googleplus
1 Google+ user

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
81 Mendeley
Title
Expanding Access to Large-Scale Genomic Data While Promoting Privacy: A Game Theoretic Approach
Published in
American Journal of Human Genetics, January 2017
DOI 10.1016/j.ajhg.2016.12.002
Pubmed ID
Authors

Zhiyu Wan, Yevgeniy Vorobeychik, Weiyi Xia, Ellen Wright Clayton, Murat Kantarcioglu, Bradley Malin

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 46 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 81 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 25%
Student > Master 9 11%
Student > Bachelor 8 10%
Researcher 8 10%
Other 7 9%
Other 15 19%
Unknown 14 17%
Readers by discipline Count As %
Computer Science 12 15%
Medicine and Dentistry 12 15%
Agricultural and Biological Sciences 9 11%
Biochemistry, Genetics and Molecular Biology 9 11%
Social Sciences 5 6%
Other 14 17%
Unknown 20 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 287. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 August 2023.
All research outputs
#123,117
of 25,420,980 outputs
Outputs from American Journal of Human Genetics
#45
of 5,881 outputs
Outputs of similar age
#2,770
of 421,859 outputs
Outputs of similar age from American Journal of Human Genetics
#2
of 49 outputs
Altmetric has tracked 25,420,980 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,881 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,859 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.