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The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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  1. Home
  3. Ten Quick Tips For Protecting Health Data Using De-identification And Perturbation Of Structured Datasets.
  1. Home
  3. Ten Quick Tips For Protecting Health Data Using De-identification And Perturbation Of Structured Datasets.

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Ten quick tips for protecting health data using de-identification and perturbation of structured datasets.

Tshikala Eddie Lulamba1, Themba Mutemaringa1,2,3, Nicki Tiffin1

  • 1South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa.

Plos Computational Biology
|September 23, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study offers practical guidance for anonymizing health data to protect patient privacy. It focuses on accessible methods to reduce re-identification risk while maintaining data utility for research and public health.

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Area of Science:

  • Health Informatics
  • Data Privacy
  • Biostatistics

Background:

  • Structured patient data is shared for operational, research, and public health purposes.
  • Protecting patient privacy and data confidentiality relies on de-identification and anonymization, but standards are lacking.
  • Current anonymization techniques can be technically complex and lack universal acceptance.

Purpose of the Study:

  • To provide practical recommendations for anonymizing structured health data.
  • To support compliance with data protection laws and reduce re-identification risk.
  • To promote responsible health data sharing for research and public health benefits.

Main Methods:

  • Applying the principle of data minimization to avoid unnecessary data granularity.
  • Implementing accessible strategies such as rounding, range replacement, jittering, and aggregation.
  • Managing date values and separating sensitive data from identifying information to prevent linkage.

    • Main Results:

    • Demonstrated practical methods for anonymizing structured health data.
    • Provided guidance on reducing re-identification risks through various data perturbation techniques.
    • Highlighted the importance of legal and governance frameworks in data anonymization.

    Conclusions:

    • Accessible anonymization strategies can be implemented without specialist expertise.
    • Responsible health data sharing is achievable while upholding patient privacy and data utility.
    • These guidelines support ethical and legal data stewardship in health research.