ArticleIEEE transactions on big data2020
Big Data Privacy in Biomedical Research.
Article in IEEE transactions on big data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS.Current allergy and asthma reports · 2025Review
- Revolutionizing Cancer Registries: The Promise and Challenges of Integrating Molecular Markers for Enhanced Patient Care and Research.Journal of registry management · 2025Article
- Augmenting small biomedical datasets using generative AI methods based on self-organizing neural networks.Briefings in bioinformatics · 2024Article
- A medical big data access control model based on smart contracts and risk in the blockchain environment.Frontiers in public health · 2024Article
- OHDSI-compliance: a set of document templates facilitating the implementation and operation of a software stack for real-world evidence generation.Frontiers in medicine · 2024Article
- Private Continuous Survival Analysis with Distributed Multi-Site Data.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2023Article
- Functional genomics data: privacy risk assessment and technological mitigation.Nature reviews. Genetics · 2022Review
- Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey.Journal of medical Internet research · 2021Article
- SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Biomedical research often involves studying patient data that contain personal information. Inappropriate use of these data might lead to leakage of sensitive information, which can put patient privacy at risk. The problem of preserving patient privacy has received increasing attentions in the era of big data. Many privacy methods have been developed to protect against various attack models. This paper reviews relevant topics in the context of biomedical research. We discuss privacy preserving technologies related to (1) record linkage, (2) synthetic data generation, and (3) genomic data privacy. We also discuss the ethical implications of big data privacy in biomedicine and present challenges in future research directions for improving data privacy in biomedical research.
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.