Haplotype-based membership inference from summary genomic data

Bu, Diyue; Tang, Haixu

doi:10.1093/bioinformatics/btab305

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…Beacon-responses are supplemented with phenotype metadata, and the attacker already knows about an individual's membership. Bu and colleagues [3] introduced a haplotype-based membership inference attack that reconstructs haplotypes using allele frequencies, as opposed to relying on a target genome. Samani and colleagues [16] introduced am method that relied on high-order SNV correlations to carry out an inference attack on Beacons using Markov models.…”

Section: A Related Workmentioning

confidence: 99%

Enabling Trade-offs in Privacy and Utility in Genomic Data Beacons and Summary Statistics

Venkatesaramani¹,

Wan²,

Malin³

et al. 2023

Preprint

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The collection and sharing of genomic data are becoming increasingly commonplace in research, clinical, and direct-to-consumer settings. The computational protocols typically adopted to protect individual privacy include sharing summary statistics, such as allele frequencies, or limiting query responses to the presence/absence of alleles of interest using web-services called Beacons.However, even such limited releases are susceptible to likelihood-ratio-based membership-inference attacks. Several approaches have been proposed to preserve privacy, which either suppress a subset of genomic variants or modify query responses for specific variants (e.g., adding noise, as in differential privacy). However, many of these approaches result in a significant utility loss, either suppressing many variants or adding a substantial amount of noise. In this paper, we introduce optimization-based approaches to explicitly trade off the utility of summary data or Beacon responses and privacy with respect to membership-inference attacks based on likelihood-ratios, combining variant suppression and modification. We consider two attack models. In the first, an attacker applies a likelihood-ratio test to make membership-inference claims. In the second model, an attacker uses a threshold that accounts for the effect of the data release on the separation in scores between individuals in the dataset and those who are not. We further introduce highly scalable approaches for approximately solving the privacy-utility tradeoff problem when information is either in the form of summary statistics or presence/absence queries. Finally, we show that the proposed approaches outperform the state of the art in both utility and privacy through an extensive evaluation with public datasets.

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Section: A Related Workmentioning

confidence: 99%

Enabling Trade-offs in Privacy and Utility in Genomic Data Beacons and Summary Statistics

Venkatesaramani¹,

Wan²,

Malin³

et al. 2023

Preprint

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“…Further studies identified that participation in different studies can lead to linking attacks and reveal sensitive information [42,43] of participants and relatives [44,45]. Other attacks that make use of haplotype frequencies have been proposed to identify participation [46,47]. Recent studies demonstrated that genetic beacons [48] (where the existence of variants in a database are queried as yes/no answers) are vulnerable to Bustamante's attacks [49] and more advanced attacks [50,51] where an adversary can identify participation of an individual in a beacon by adversarial querying, which may impact biobank scale databases.…”

Section: Introductionmentioning

confidence: 99%

ProxyTyper: Generation of Proxy Panels for Privacy-aware Outsourcing of Genotype Imputation

Zhi,

Jiang,

Harmanci

2023

Preprint

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One of the major challenges in genomic data sharing is protecting the privacy of participants. Numerous studies demonstrated that genetic data and related summary statistics can be used for identifying individuals. These studies led to a strong chilling effect on researchers that hindered collaborative data sharing. Consequently, population-level genetic databases are often siloed in central repositories with complex and burdensome data usage agreements. While cryptographic methods that are provably secure have been developed, these methods require high-level expertise in security and depend on large computational resources.To fill the methodological gap in this domain, we present ProxyTyper, a set of data protection mechanisms to generate “proxy-panels” from sensitive haplotype-level genetic datasets. ProxyTyper uses haplotype sampling, allele hashing, and anonymization to protect the genetic variant coordinates, genetic maps, and chromosome-wide haplotypes. These mechanisms can provide strong deterrence against honest-but-curious entities and well-known re-identification and linking attacks. The proxy panels can be used as input to existing tools without any modifications to the underlying algorithms. We focus on building proxy-panels for genotype imputation to protect typed and untyped variants. We demonstrate that proxy-based imputation provides protection against well-known attacks with a minor decrease of imputation accuracy for variants in wide range of allele frequencies.

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“…Further, populationlevel omics (e.g., population genomics) hold tremendous potential for classifying individuals based on allelic diversity and identifying genetically-related individuals (Lippert et al, 2017). Such strategies can help calculate homozygosity and inbreeding coefficients (Ghoreishifar et al, 2020;Sumreddee et al, 2021) for designing appropriate breeding programs to maintain genetic diversity and avoid inbreeding depression (Alemu et al, 2021;Bu et al, 2021). However, while many WGS databases and consortiums have been formed in humans (Zhang et al, 2018) (GenomeAsia 100K Consortium, 2019, no high-resolution database of population-level genetic variants is available for animals.…”

Section: Introductionmentioning

confidence: 99%

Applications of Omics Technology for Livestock Selection and Improvement

et al. 2022

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Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

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Haplotype-based membership inference from summary genomic data

Cited by 6 publications

References 31 publications

Enabling Trade-offs in Privacy and Utility in Genomic Data Beacons and Summary Statistics

Enabling Trade-offs in Privacy and Utility in Genomic Data Beacons and Summary Statistics

ProxyTyper: Generation of Proxy Panels for Privacy-aware Outsourcing of Genotype Imputation

Applications of Omics Technology for Livestock Selection and Improvement

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