Search research articles

Search research articles

Related Experiment Videos

Using Boolean reasoning to anonymize databases.

A Ohrn¹, L Ohno-Machado

¹Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim. aleks@idi.ntnu.no

Artificial Intelligence in Medicine

|April 17, 1999

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

Training in Health Informatics in Brazil.

Yearbook of medical informatics·2016

Same author

Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership.

Applied clinical informatics·2015

Same author

Health IT and clinical decision support systems: human factors and successful adoption.

Journal of the American Medical Informatics Association : JAMIA·2014

Same author

"Big data" and the electronic health record.

Yearbook of medical informatics·2014

Same author

Confluence of disciplines in health informatics: an international perspective.

Methods of information in medicine·2011

Same author

Feasibility evaluation of Smart Stretcher to improve patient safety during transfers.

Methods of information in medicine·2010

Same journal

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Artificial intelligence in medicine·2026

Same journal

R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform.

Artificial intelligence in medicine·2026

Same journal

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Artificial intelligence in medicine·2026

Same journal

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Artificial intelligence in medicine·2026

Same journal

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Artificial intelligence in medicine·2026

Same journal

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

Artificial intelligence in medicine·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study introduces a novel algorithm using Boolean reasoning and cell suppression to anonymize databases, particularly for sensitive medical information. The method allows customizable anonymity levels to protect data privacy and prevent misuse.

Area of Science:

Computer Science
Information Security
Medical Informatics

Background:

Increasing digitization of medical records necessitates robust data anonymization techniques.
Privacy concerns and potential misuse of confidential health information are significant challenges.
Existing methods may not offer sufficient granularity or adaptability for diverse sharing scenarios.

Purpose of the Study:

To develop and present a theoretically grounded algorithm for database anonymization.
To enable tailored anonymity levels based on recipient trust and specific data needs.
To prevent deterministic inferences about sensitive fields in anonymized datasets.

Main Methods:

Utilizes Boolean reasoning to identify and suppress data cells.

Related Experiment Videos

Employs a cell suppression technique for anonymization.

Algorithm allows for adjustable anonymity degrees and granular control.

Main Results:

A well-founded algorithm for making databases anonymous via cell suppression is proposed.
The method allows for customizable anonymity tailored to recipient needs and trust levels.
The algorithm effectively blocks deterministic inferences about sensitive database fields.

Conclusions:

Boolean reasoning offers a powerful approach to medical database anonymization.
The proposed algorithm provides a flexible and effective solution for protecting sensitive data.
This method enhances data security for sharing electronic medical records and repositories.