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Related Experiment Video

Updated: Jun 16, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Confidentiality modelling and simulation and validation in a simplified database access.

Marn-Ling Shing¹, Chen-Chi Shing, Kuo Lane Chen

¹Early Child Education Department and Program of Child Development, Taipei Municipal University of Education, 1 Ai-Kuo West Road, Taipei, Taiwan, ROC.

International Journal of Computational Biology and Drug Design

|January 22, 2010

Summary

This summary is machine-generated.

This study addresses database confidentiality by modeling security policies with semi-Markov chains. A simulation validated the approach, enhancing data privacy management.

Related Experiment Videos

Last Updated: Jun 16, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Computer Science
Information Security

Background:

Database access models often prioritize data integrity over confidentiality, especially with insensitive data.
Management of database privacy is frequently overlooked in simplified secured database access models.
Both privileged and public groups may access data without sufficient distribution controls.

Purpose of the Study:

To investigate data confidentiality management in simplified secured database access models.
To propose a method for modeling database security policies to ensure confidentiality.
To address the neglect of database privacy management in favor of data integrity.

Main Methods:

Utilized semi-Markov chains to model the database security policy.
Developed a simulation experiment to test and validate the proposed model.
Focused on applying confidentiality policies within the access control framework.

Main Results:

The semi-Markov chain model effectively represents the security policy for database access.
Simulation results confirmed the model's capability to manage data confidentiality.
Demonstrated a viable approach to integrate confidentiality management into existing database security.

Conclusions:

Semi-Markov chains provide a robust framework for modeling and enforcing database confidentiality policies.
The proposed model offers a practical solution for enhancing data privacy in database systems.
Emphasizes the importance of addressing data confidentiality alongside data integrity for comprehensive security.