ReviewPatterns (New York, N.Y.)2021
Differential privacy for public health data: An innovative tool to optimize information sharing while protecting data confidentiality.
Review in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Prostate cancer research on social media platforms: a bibliometric and thematic analysis.Frontiers in oncology · 2026Pooled it
- Wearable Sensing for Clinical Physiology Monitoring: Emerging Paradigms.Physiology (Bethesda, Md.) · 2026Review
- Next-generation sequencing: Driving a paradigm shift from pathogen detection to systems-level analysis.Infectious medicine · 2026Article
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- Advancements, challenges, and prospects of explainable AI in sleep disordered breathing.Sleep & breathing = Schlaf & Atmung · 2026Review
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Keeping generative artificial intelligence reliable in omics biology.Patterns (New York, N.Y.) · 2026Article
- Limitations of current copyright frameworks for large language models trained on scientific literature.Frontiers in artificial intelligence · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Data Protection in Dynamic Consent Management Systems: Formalizing Privacy and Security Definitions with Differential Privacy, Decentralization, and Zero-Knowledge Proofs.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) has highlighted the need for the timely collection and sharing of public health data. It is important that data sharing is balanced with protecting confidentiality. Here we discuss an innovative mechanism to protect health data, called differential privacy. Differential privacy is a mathematically rigorous definition of privacy that aims to protect against all possible adversaries. In layperson's terms, statistical noise is applied to the data so that overall patterns can be described, but data on individuals are unlikely to be extracted. One of the first use cases for health data in Australia is the development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), which provides proof of concept for the use of this technology in the health sector. If successful, this will benefit future sharing of public health data.
Indexed as
Identifiers
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.