Routes for breaching and protecting genetic privacy

By Yaniv Erlich, Arvind Narayanan. Nature reviews genetics, 2013

472 citations recorded by the source corpus.

We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these data sets is vital for progress in biomedical research. However, a growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, indicate the underlying assumptions, and assess their technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications.

Subfields: Artificial Intelligence, Public Health, Environmental and Occupational Health, Safety Research

View Paper: https://www.semanticscholar.org/paper/62215b59b3edb6eed7e1ff4f9d809c80a56a4ef7

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Citations in the corpus

Data Safe Havens in health research and healthcare (2015, background): “the use of anonymization and de-identification mechanisms can sometimes vitiate the utility of data for certain important research purposes (Erlich and Narayanan, 2014).”
Big Data: Astronomical or Genomical? (2015, background): “Homomorphic encryption systems, in which encrypted data can be analyzed and manipulated for certain controlled queries without disclosing the raw data, are currently too computationally expensive for widespread use, but these and related cryptographic techniques are promising areas of research [39].”
Machine learning and genomics: precision medicine versus patient privacy (2018, background): “This can be achieved by informing participants of the intended and actual use of their data; giving them control over future use of their data; and informing them of the outcomes of the studies they have participated in [67].”
Private genome analysis through homomorphic encryption (2015, background): “, name, date of birth or address) are removed from genomic data, one can often recover the identity information [3-5].”
Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks (2017, background): “…(Loukides et al., 2010), lab tests (Atreya et al., 2013), visits across healthcare providers (Malin and Sweeney, 2004), and genomic variants (Erlich and Narayanan, 2014)) To mitigate re-identification vulnerabilities, researchers in the statistical disclosure control community have…”
Robust Traceability from Trace Amounts (2015, background): “, allele frequencies, genetic frequencies, and various quantitive phenotypes such as cholesterol levels [1], [29], [32], [33]; see [34] for a survey.”
CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions (2024, methodology): “We provide software for offline scoring especially for users that are legally required to score SNV and InDel variants on their own systems ( 18 ,74 ).”
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations (2016, unknown): “These findings have led the NIH, among others, to place much of its aggregate genomic data into repositories and require researchers to apply for access (Erlich and Narayanan, 2014).”
Understanding the Promises and Hurdles of Metagenomic Next-Generation Sequencing as a Diagnostic Tool for Infectious Diseases (2017, background): “introduces privacy concerns and requires HIPAA compliant storage tools [33].”
Identity inference of genomic data using long-range familial searches (2018, background): “search, has been predicted before (8) and offers a powerful”
Realizing privacy preserving genome-wide association studies (2016, background): “Unfortunately, there is growing concern that the results of these studies might lead to loss of privacy for those who participate in them (Erlich and Narayanan, 2014; Homer et al. , 2008; Lumley and Rice, 2010).”
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees (2018, background): “The most common way to mitigate the privacy risk of sharing sensitive records is to de-identify the records - but it is by now well-known that records that have been de-identified can be easily re-identified by linking them to other identifiable datasets [30; 13; 24; 22; 14].”