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Controlling the signal: Practical privacy protection of genomic data sharing through Beacon services

Overview of attention for article published in BMC Medical Genomics, July 2017
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Title
Controlling the signal: Practical privacy protection of genomic data sharing through Beacon services
Published in
BMC Medical Genomics, July 2017
DOI 10.1186/s12920-017-0282-1
Pubmed ID
Authors

Zhiyu Wan, Yevgeniy Vorobeychik, Murat Kantarcioglu, Bradley Malin

Abstract

Genomic data is increasingly collected by a wide array of organizations. As such, there is a growing demand to make summary information about such collections available more widely. However, over the past decade, a series of investigations have shown that attacks, rooted in statistical inference methods, can be applied to discern the presence of a known individual's DNA sequence in the pool of subjects. Recently, it was shown that the Beacon Project of the Global Alliance for Genomics and Health, a web service for querying about the presence (or absence) of a specific allele, was vulnerable. The Integrating Data for Analysis, Anonymization, and Sharing (iDASH) Center modeled a track in their third Privacy Protection Challenge on how to mitigate the Beacon vulnerability. We developed the winning solution for this track. This paper describes our computational method to optimize the tradeoff between the utility and the privacy of the Beacon service. We generalize the genomic data sharing problem beyond that which was introduced in the iDASH Challenge to be more representative of real world scenarios to allow for a more comprehensive evaluation. We then conduct a sensitivity analysis of our method with respect to several state-of-the-art methods using a dataset of 400,000 positions in Chromosome 10 for 500 individuals from Phase 3 of the 1000 Genomes Project. All methods are evaluated for utility, privacy and efficiency. Our method achieves better performance than all state-of-the-art methods, irrespective of how key factors (e.g., the allele frequency in the population, the size of the pool and utility weights) change from the original parameters of the problem. We further illustrate that it is possible for our method to exhibit subpar performance under special cases of allele query sequences. However, we show our method can be extended to address this issue when the query sequence is fixed and known a priori to the data custodian, so that they may plan stage their responses accordingly. This research shows that it is possible to thwart the attack on Beacon services, without substantially altering the utility of the system, using computational methods. The method we initially developed is limited by the design of the scenario and evaluation protocol for the iDASH Challenge; however, it can be improved by allowing the data custodian to act in a staged manner.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 39%
Other 5 16%
Student > Master 4 13%
Researcher 3 10%
Professor > Associate Professor 2 6%
Other 3 10%
Unknown 2 6%
Readers by discipline Count As %
Computer Science 10 32%
Biochemistry, Genetics and Molecular Biology 4 13%
Agricultural and Biological Sciences 4 13%
Engineering 2 6%
Nursing and Health Professions 1 3%
Other 4 13%
Unknown 6 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 09 August 2017.
All research outputs
#10,281,980
of 11,590,856 outputs
Outputs from BMC Medical Genomics
#478
of 532 outputs
Outputs of similar age
#224,287
of 265,520 outputs
Outputs of similar age from BMC Medical Genomics
#6
of 7 outputs
Altmetric has tracked 11,590,856 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 532 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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