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, Zhiyuan Chen, Aryya Gangopadhyay
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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