ArticlePloS one2021
Human microbiome privacy risks associated with summary statistics.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- nf-core/detaxizer: a benchmarking study for decontamination from human sequences.NAR genomics and bioinformatics · 2025Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recognizing that microbial community composition within the human microbiome is associated with the physiological state of the host has sparked a large number of human microbiome association studies (HMAS). With the increasing size of publicly available HMAS data, the privacy risk is also increasing because HMAS metadata could contain sensitive private information. I demonstrate that a simple test statistic based on the taxonomic profiles of an individual's microbiome along with summary statistics of HMAS data can reveal the membership of the individual's microbiome in an HMAS sample. In particular, species-level taxonomic data obtained from small-scale HMAS can be highly vulnerable to privacy risk. Minimal guidelines for HMAS data privacy are suggested, and an assessment of HMAS privacy risk using the simulation method proposed is recommended at the time of study design.
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Registered trials
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.