ArticleJournal of the American Medical Informatics Association : JAMIA
Protecting count queries in study design.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Privacy technology to support data sharing for comparative effectiveness research: a systematic review.Medical care · 2013Pooled it
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Differential privacy in health research: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis.Journal of medical Internet research · 2021Article
- Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018Article
- Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations.Cell systems · 2016Article
- Realizing privacy preserving genome-wide association studies.Bioinformatics (Oxford, England) · 2016Article
- Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation.Frontiers in neuroinformatics · 2014Article
- Privacy preserving RBF kernel support vector machine.BioMed research international · 2014Article
- Differentially private distributed logistic regression using private and public data.BMC medical genomics · 2014Article
- Article
- Sharing data for the public good and protecting individual privacy: informatics solutions to combine different goals.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- SHARE: system design and case studies for statistical health information release.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveToday's clinical research institutions provide tools for researchers to query their data warehouses for counts of patients. To protect patient privacy, counts are perturbed before reporting; this compromises their utility for increased privacy. The goal of this study is to extend current query answer systems to guarantee a quantifiable level of privacy and allow users to tailor perturbations to maximize the usefulness according to their needs.
methodsA perturbation mechanism was designed in which users are given options with respect to scale and direction of the perturbation. The mechanism translates the true count, user preferences, and a privacy level within administrator-specified bounds into a probability distribution from which the perturbed count is drawn.
resultsUsers can significantly impact the scale and direction of the count perturbation and can receive more accurate final cohort estimates. Strong and semantically meaningful differential privacy is guaranteed, providing for a unified privacy accounting system that can support role-based trust levels. This study provides an open source web-enabled tool to investigate visually and numerically the interaction between system parameters, including required privacy level and user preference settings.
conclusionsQuantifying privacy allows system administrators to provide users with a privacy budget and to monitor its expenditure, enabling users to control the inevitable loss of utility. While current measures of privacy are conservative, this system can take advantage of future advances in privacy measurement. The system provides new ways of trading off privacy and utility that are not provided in current study design systems.
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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.