A Privacy Protection Model for Patient Data With Multiple Sensitive Attributes

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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

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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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.

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A Privacy Protection Model for Patient Data With Multiple Sensitive Attributes

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