ArticleJournal of the American Medical Informatics Association : JAMIA2023
Managing re-identification risks while providing access to the All of Us research program.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Advancing Real-World Evidence Generation: Growth and Lessons Learned from the Early Years of Cosmos.ACI open · 2026Review
- ClinFly: an all-in-one method to translate, de-identify, and summarize medical reports in HPO format.NAR genomics and bioinformatics · 2025Article
- Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm.NPJ digital medicine · 2025Article
- COLA-GLM: collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data.NPJ digital medicine · 2025Article
- Navigating sexual orientation and gender identity data privacy concerns in United States genetics practices.Journal of genetic counseling · 2025Article
- DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- A decade of research on genetic privacy: the findings of the GetPreCiSe Center at Vanderbilt University.Frontiers in genetics · 2025Review
- Can Open Science Advance Health Justice? Genomic Research Dissemination in the Evolving Data-Sharing Landscape.The Hastings Center report · 2024Article
- Returning value to communities from the All of Us Research Program through innovative approaches for data use, analysis, dissemination, and research capacity building.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The Costs of Anonymization: Case Study Using Clinical Data.Journal of medical Internet research · 2024Article
- Misaligned Expectations: Motivations of Medically Underserved People to Enroll in the All of Us Research Program.Ethics & human researchArticle
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
objectiveThe All of Us Research Program makes individual-level data available to researchers while protecting the participants' privacy. This article describes the protections embedded in the multistep access process, with a particular focus on how the data was transformed to meet generally accepted re-identification risk levels.
methodsAt the time of the study, the resource consisted of 329 084 participants. Systematic amendments were applied to the data to mitigate re-identification risk (eg, generalization of geographic regions, suppression of public events, and randomization of dates). We computed the re-identification risk for each participant using a state-of-the-art adversarial model specifically assuming that it is known that someone is a participant in the program. We confirmed the expected risk is no greater than 0.09, a threshold that is consistent with guidelines from various US state and federal agencies. We further investigated how risk varied as a function of participant demographics.
resultsThe results indicated that 95th percentile of the re-identification risk of all the participants is below current thresholds. At the same time, we observed that risk levels were higher for certain race, ethnic, and genders.
conclusionsWhile the re-identification risk was sufficiently low, this does not imply that the system is devoid of risk. Rather, All of Us uses a multipronged data protection strategy that includes strong authentication practices, active monitoring of data misuse, and penalization mechanisms for users who violate terms of service.
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What 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.