ArticlePLoS computational biology2025
Ten quick tips for protecting health data using de-identification and perturbation of structured datasets.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Patient-Initiated Permanent Deletion of Their Electronic Health Record Data: implications for Artificial Intelligence and Big Data in Healthcare : Clyde T. Matava, MBChB, MMed, MHSc.Journal of medical systems · 2026Article
- AI in practice: a multilingual survey of 2025 BioHackathon participants.GigaByte (Hong Kong, China) · 2026Article
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
Structured patient data generated within the health data ecosystem are shared both internally for operational use and also externally for research and public health benefit. Protecting individual privacy and health data confidentiality in these contexts relies on data de-identification and anonymisation, although there are no universally accepted standards for these processes and the techniques involved can be technically complex. We present practical recommendations grounded in the principle of data minimisation-avoiding unnecessary granularity and identifying variables that could lead to re-identification when combined with other datasets. We provide practical guidance for anonymising and perturbing structured health data in ways that support compliance with data protection laws, describing technical and operational methods for reducing re-identification risk that include rounding numerical values, replacing precise values with ranges, adding jitter to numeric fields, aggregating data, management of date values and separating sensitive fields from identifying data to prevent linkage leading to re-identification. While some methods require advanced technical knowledge, we focus here on accessible strategies that can be implemented without specialist expertise, recognising the importance of the legal and governance frameworks in which anonymisation occurs. These guidelines support researchers, data managers and institutions in sharing health data responsibly, maintaining data utility while upholding privacy and promoting ethical and legal data stewardship for data-driven health 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.