A Privacy Protection Model for Patient Data with Multiple Sensitive Attributes

Tamas S. Gal, Zhiyuan Chen and Aryya Gangopadhyay
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Tamas S. Gal: Univeristy of Maryland Baltimore County (UMBC), USA
Zhiyuan Chen: Univeristy of Maryland Baltimore County (UMBC), USA
Aryya Gangopadhyay: Univeristy of Maryland Baltimore County (UMBC), USA

International Journal of Information Security and Privacy (IJISP), 2008, vol. 2, issue 3, 28-44

Abstract: The identity of patients must be protected when patient data are 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 article shows that although the K-anonymity model can be trivially extended to multiple sensitive attributes, the 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. We 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 our approach.

Date: 2008
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