Evidence map›Paper›PMID 33381836›Full record

ArticleBioinformatics (Oxford, England)2020

The effect of kinship in re-identification attacks against genomic data sharing beacons.

Kerem Ayoz, Miray Aysen, Erman Ayday, A Ercument Cicek

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025
    Article
  4. Article
  5. Article
  6. Review
  7. Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2022
    Article
  8. Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kerem AyozComputer Engineering Department, Bilkent University, Ankara 06800, Turkey.
Miray AysenComputer Engineering Department, Bilkent University, Ankara 06800, Turkey.
Erman AydayComputer Engineering Department, Bilkent University, Ankara 06800, Turkey.
A Ercument CicekComputer Engineering Department, Bilkent University, Ankara 06800, Turkey.

Funding

Privacy Challenges of Genomic Data-Sharing Beacons and SolutionsR01LM013429 · NLM · CASE WESTERN RESERVE UNIVERSITY · PI AYDAY, ERMAN · 2020 to 2023
$1.2M
NLM NIH HHS R01 LM013429
6 · The paper itself

Abstract

motivationBig data era in genomics promises a breakthrough in medicine, but sharing data in a private manner limit the pace of field. Widely accepted 'genomic data sharing beacon' protocol provides a standardized and secure interface for querying the genomic datasets. The data are only shared if the desired information (e.g. a certain variant) exists in the dataset. Various studies showed that beacons are vulnerable to re-identification (or membership inference) attacks. As beacons are generally associated with sensitive phenotype information, re-identification creates a significant risk for the participants. Unfortunately, proposed countermeasures against such attacks have failed to be effective, as they do not consider the utility of beacon protocol.

resultsIn this study, for the first time, we analyze the mitigation effect of the kinship relationships among beacon participants against re-identification attacks. We argue that having multiple family members in a beacon can garble the information for attacks since a substantial number of variants are shared among kin-related people. Using family genomes from HapMap and synthetically generated datasets, we show that having one of the parents of a victim in the beacon causes (i) significant decrease in the power of attacks and (ii) substantial increase in the number of queries needed to confirm an individual's beacon membership. We also show how the protection effect attenuates when more distant relatives, such as grandparents are included alongside the victim. Furthermore, we quantify the utility loss due adding relatives and show that it is smaller compared with flipping based techniques.

Indexed as

GenomicsInformation DisseminationFamilyHumansPhenotype

Identifiers

PMID33381836
PMCPMC7773481

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.