On genomics, kin, and privacy

Telenti, Amalio; Ayday, Erman; Hubaux, J.-P.

doi:10.12688/f1000research.3817.1

Cited by 9 publications

(8 citation statements)

References 9 publications

(12 reference statements)

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“…Today, where online services with personal images coexist with large genetic databases, such as 23andMe, associating genomic data to physical traits (e.g., eye and skin color) obtains particular relevance (6). In fact, genome data may be linked to metadata through online social networks and services, thus complicating the protection of genome privacy (7). Revealing the identity of genome data may not only affect the contributor, but may also compromise the privacy of family members (8).…”

Section: Identification Of Individuals By Trait Prediction Using Wholmentioning

confidence: 99%

“…Although individually, some of these phenotypes have been evaluated (1,15), we propose an algorithm that integrates each predictive model to match a deidentified WGS sample to phenotypic and demographic information at higher accuracy. When the source of the phenotypic data is of known identity, this procedure may reidentify a genomic sample, raising implications for genomic privacy (6)(7)(8)(9)16).…”

Section: Identification Of Individuals By Trait Prediction Using Wholmentioning

confidence: 99%

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Identification of individuals by trait prediction using whole-genome sequencing data

Lippert

Sabatini

Maher

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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142

119

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Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.

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Section: Identification Of Individuals By Trait Prediction Using Wholmentioning

confidence: 99%

Section: Identification Of Individuals By Trait Prediction Using Wholmentioning

confidence: 99%

Identification of individuals by trait prediction using whole-genome sequencing data

Lippert

Sabatini

Maher

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

142

119

View full text Add to dashboard Cite

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“…Much of these attacks implicate and create discrimination and stigmatization risks to individuals and their families [32][33][34][35]. Therefore genetic kinship estimation presents numerous unaccounted challenges regarding individual and kin privacy [32,34,36].…”

Section: Introductionmentioning

confidence: 99%

Privacy-Aware Kinship Inference in Admixed Populations using Projection on Reference Panels

Wang

Kim

et al. 2022

Preprint

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Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in 3rd party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization. Here, we make use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in 2 different sites while genotype data is kept confidential.

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“…On another note, many individuals share their genetic information publicly on the online genetic genealogy portals. However, several recent studies have pointed out that genetic data is highly identifying and it can be used to link the individual's information to unexpected databases [19], [20]. Moreover, recent advances in usage of DNA as incriminating forensic evidence to solve high-profile cases brings many ethical questions that may cause concerns for these individuals [21], [22].…”

Section: Introductionmentioning

confidence: 99%

HaploHide: A Data Hiding Framework for Privacy Enhanced Sharing of Personal Genetic Data

Harmanci

Jiang²,

Zhi³

2019

Preprint

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Personal genetic data is becoming a digital commodity as millions of individuals have direct access to and control of their genetic information. This information must be protected as it can be used for reidentification and potential discrimination of individuals and relatives. While there is a great incentive to share and use genetic information, there are limited number of practical approaches for protecting it when individuals would like to make use of their genomes in clinical and recreational settings. To enable privacy-enhanced usage of genomic data by individuals, we propose a crowd-blending-based framework where portions of the individual's haplotype is "hidden" within a large sample of other haplotypes. The hiding framework is motivated by the existence of large-scale population panels that we utilize for generation of the crowd of haplotypes in which the individual's haplotype is hidden. We demonstrate the usage of hiding in two different scenarios: Sharing of variant alleles on genes and sharing of GWAS variant alleles. We evaluate hiding framework by testing reidentification of hidden individuals using numerous measures of individual reidentification. In these settings, we discuss how effective hiding can be accomplished when the adversary does not have access to auxiliary identifying information. Compared to the existing approaches for protecting privacy, which require substantial changes in the computational infrastructure, e.g., homomorphic encryption, hiding-based framework does not incur any changes to the infrastructure. However, the processing must be performed for every sample in the crowd and therefore data processing cost will increase as the crowd size increases.

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On genomics, kin, and privacy

Cited by 9 publications

References 9 publications

Identification of individuals by trait prediction using whole-genome sequencing data

Identification of individuals by trait prediction using whole-genome sequencing data

Privacy-Aware Kinship Inference in Admixed Populations using Projection on Reference Panels

HaploHide: A Data Hiding Framework for Privacy Enhanced Sharing of Personal Genetic Data

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