IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Privacy Protection Model for Patient Data With Multiple Sensitive Attributes

A Privacy Protection Model for Patient Data With Multiple Sensitive Attributes
View Sample PDF
Author(s): Tamas S. Gal (University of Maryland Baltimore County (UMBC), USA), Zhiyuan Chen (University of Maryland Baltimore County (UMBC), USA) and Aryya Gangopadhyay (University of Maryland Baltimore County (UMBC), USA)
Copyright: 2011
Pages: 17
Source title: Pervasive Information Security and Privacy Developments: Trends and Advancements
Source Author(s)/Editor(s): Hamid Nemati (The University of North Carolina at Greensboro, USA)
DOI: 10.4018/978-1-61692-000-5.ch004

Purchase

View A Privacy Protection Model for Patient Data With Multiple Sensitive Attributes on the publisher's website for pricing and purchasing information.

Abstract

The identity of patients must be protected when patient data is shared. The two most commonly used models to protect identity of patients are L-diversity and K-anonymity. However, existing work mainly considers data sets with a single sensitive attribute, while patient data often contain multiple sensitive attributes (e.g., diagnosis and treatment). This chapter shows that although the K-anonymity model can be trivially extended to multiple sensitive attributes, L-diversity model cannot. The reason is that achieving L-diversity for each individual sensitive attribute does not guarantee L-diversity over all sensitive attributes. The authors propose a new model that extends L-diversity and K-anonymity to multiple sensitive attributes and propose a practical method to implement this model. Experimental results demonstrate the effectiveness of this approach.

Related Content

. © 2021. 31 pages.
. © 2021. 23 pages.
. © 2021. 20 pages.
. © 2021. 29 pages.
. © 2021. 27 pages.
. © 2021. 20 pages.
. © 2021. 20 pages.
Body Bottom