A Privacy Protection Model for Patient Data with Multiple Sensitive Attributes

A Privacy Protection Model for Patient Data with Multiple Sensitive Attributes

Tamas S. Gal (Univeristy of Maryland Baltimore County (UMBC), USA), Zhiyuan Chen (Univeristy of Maryland Baltimore County (UMBC), USA) and Aryya Gangopadhyay (Univeristy of Maryland Baltimore County (UMBC), USA)
Copyright: © 2008 |Pages: 17
DOI: 10.4018/jisp.2008070103
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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.

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