Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Can a database be anonymous?

C Quantin1, F A Allaert, P d'Athis

  • 1Dijon University Hospital, Medical Informatics Department, France.

Studies in Health Technology and Informatics
|March 21, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Changes in Hospitalization for Eating Disorders and Related Suicidal Risk, Following COVID-19 Pandemic.

Depression and anxiety·2026
Same author

Reliability of a French hospital administrative database to measure self-harm in epidemiology studies.

General hospital psychiatry·2024
Same author

Author reply.

BJOG : an international journal of obstetrics and gynaecology·2024
Same author

[Comparison of mortality of lung cancer resections in France to other European countries].

Revue des maladies respiratoires·2022
Same author

Prolonged impact of the COVID-19 pandemic on self-harm hospitalizations in France: A nationwide retrospective observational study.

European psychiatry : the journal of the Association of European Psychiatrists·2022
Same author

Revue d'epidemiologie et de sante publique·2022
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Protecting sensitive data requires anonymity. While statistical methods alter data, encryption secures information without modification, simplifying processing and ensuring privacy.

Area of Science:

  • Computer Science
  • Information Security
  • Data Privacy

Background:

  • Sensitive data often contains indirect identifiers, necessitating anonymization for privacy protection.
  • Current anonymization techniques, particularly statistical methods, involve data perturbation, leading to processing challenges.
  • Existing privacy-preserving methods may introduce complexities in data handling and analysis.

Purpose of the Study:

  • To evaluate the challenges associated with statistical methods for data anonymization.
  • To explore encryption as an alternative approach for ensuring data confidentiality.
  • To compare the data processing requirements of statistical anonymization versus encryption.

Main Methods:

  • Review of anonymization techniques, focusing on statistical perturbation methods.

Related Experiment Videos

  • Analysis of encryption algorithms for data confidentiality.
  • Comparative assessment of data processing difficulties between the two approaches.

    • Main Results:

    • Statistical anonymization methods require data perturbation, which complicates data processing.
    • Encryption methods preserve data confidentiality without necessitating data modification.
    • Encryption offers a potentially simpler approach to data handling compared to perturbed statistical data.

    Conclusions:

    • Encryption provides a viable alternative to statistical methods for sensitive data privacy.
    • The avoidance of data modification in encryption simplifies data processing and analysis.
    • Implementing encryption can mitigate the data processing difficulties inherent in statistical anonymization techniques.