ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Detecting the Presence of an Individual in Phenotypic Summary Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05277116 (The Electronic Medical Records and GEnomics), which is not on this map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The Electronic Medical Records and GEnomics (eMERGE) Network Genomic Risk Assessment
Who cites it
4 citing papers in PubMed.
- A decade of research on genetic privacy: the findings of the GetPreCiSe Center at Vanderbilt University.Frontiers in genetics · 2025Review
- Re-identification of individuals in genomic datasets using public face images.Science advances · 2021Article
- Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis.Journal of medical Internet research · 2021Article
- Biomedical Research Cohort Membership Disclosure on Social Media.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
As the quantity and detail of association studies between clinical phenotypes and genotypes grows, there is a push to make summary statistics widely available. Genome wide summary statistics have been shown to be vulnerable to the inference of a targeted individual's presence. In this paper, we show that presence attacks are feasible with phenome wide summary statistics as well. We use data from three healthcare organizations and an online resource that publishes summary statistics. We introduce a novel attack that achieves over 80% recall and precision within a population of 16,346, where 8,173 individuals are targets. However, the feasibility of the attack is dependent on the attacker's knowledge about 1) the targeted individual and 2) the reference dataset. Within a population of over 2 million, where 8,173 individuals are targets, our attack achieves 31% recall and 17% precision. As a result, it is plausible that sharing of phenomic summary statistics may be accomplished with an acceptable level of privacy risk.
Indexed as
Identifiers
30815118PMC6371366What OpenQuestion holds
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.