Article

GenProtect-V: A Variational Inference-based Framework for Privacy-Preserving Synthetic Human Genomic Data Generation

2025-12-29

Abstract excerpt

The generation of synthetic human genomic data offers immense potential for biomedical research and data sharing, while theoretically safeguarding individual privacy. However, existing methods, including deep generative models, struggle to achieve a robust balance between data utility and privacy protection. State-of-the-art evaluations like PRISM-G reveal vulnerabilities such as proximity, kinship replay, and tra...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 234d6688-f72d-5eb3-80af-821ba79e04a6
DOI: 10.20944/preprints202512.2461.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

GenProtect-V: A Variational Inference-based Framework for Privacy-Preserving Synthetic Human Genomic Data GenerationDOI 10.20944/preprints202512.2461.v1Read

PRISM-G: an interpretable privacy scoring framework for assessing risk in synthetic human genome dataSemantic proximity Read

Latent generative modeling of long genetic sequences with GANsSemantic proximity Read

Learning inherent genetic patterns and trait associations with deep generative models for discrete genotype simulation.Semantic proximity Read

Creating Artificial Human Genomes Using Generative ModelsSemantic proximity Read

Creating artificial human genomes using generative neural networks.Semantic proximity Read

Deep Generative Models for Discrete Genotype SimulationSemantic proximity Read

Towards Useful and Private Synthetic Omics: Community Benchmarking of Generative Models for Transcriptomics DataSemantic proximity Read

Privacy-Preserving Pangenome GraphsSemantic proximity Read

Generating realistic artificial Human genomes using adversarial autoencodersSemantic proximity Read

Synthetic observations from deep generative models and binary omics data with limited sample size.Semantic proximity Read

Private Information Leakage from Polygenic Risk ScoresSemantic proximity Read

<i>In silico</i> generation of synthetic cancer genomes using generative AISemantic proximity Read