ArticleScientific data2024
Semi-local Time sensitive Anonymization of Clinical Data.
Article in Scientific data, 2024. 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.
- Quantifying the Impact of Anonymization-Induced Clinical Data Quality Loss: Methodological Quantitative Case Study Using Primary Diagnosis Codes and Hospital Length of Stay.JMIR medical informatics · 2026Article
- Challenges of identification and anonymity in time-continuous data from medical environments.Frontiers in digital health · 2025Article
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
A method for the anonymization of time-continuous data, which preserves the relation between the time- and value dimension is proposed in this work. The approach protects against linking- and distribution attacks by providing k-anonymity and t-closeness. Distributions can be generated from given sets using Distribution Clustering, according to the similarity of the curves, which serve as a replacement for the population distribution. Before the data is anonymized, it is split along the time-axis using Windowed Fréchet Splitting, to reduce the duration and information loss. The proposed approach employs bucketization using the Fréchet distance with an implicit maximum cost and implied t for closeness and multiple redistribution phases. The information loss, median relative error and achieved t for the closeness is low, and the runtime was reduced with the introduction of semi-local decisions.
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.