ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2011
Attribute Utility Motivated k-anonymization of datasets to support the heterogeneous needs of biomedical researchers.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Algorithms to anonymize structured medical and healthcare data: A systematic review.Frontiers in bioinformatics · 2022Pooled it
- Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review.Journal of medical Internet research · 2019Article
- A roadmap towards personalized immunology.NPJ systems biology and applications · 2018Article
- ARX--A Comprehensive Tool for Anonymizing Biomedical Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to support the increasing need to share electronic health data for research purposes, various methods have been proposed for privacy preservation including k-anonymity. Many k-anonymity models provide the same level of anoymization regardless of practical need, which may decrease the utility of the dataset for a particular research study. In this study, we explore extensions to the k-anonymity algorithm that aim to satisfy the heterogeneous needs of different researchers while preserving privacy as well as utility of the dataset. The proposed algorithm, Attribute Utility Motivated k-anonymization (AUM), involves analyzing the characteristics of attributes and utilizing them to minimize information loss during the anonymization process. Through comparison with two existing algorithms, Mondrian and Incognito, preliminary results indicate that AUM may preserve more information from original datasets thus providing higher quality results with lower distortion.
Indexed as
Identifiers
22195223PMC3243250What 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.