Gal, Tamas S.Chen, ZhiyuanGangopadhyay, Aryya2021-08-162021-08-162008-07Gal, Tamas; Chen, Zhiyuan; Gangopadhyay, Aryya; A Privacy Protection Model for Patient Data with Multiple Sensitive Attributes; International Journal of Information Security and Privacy (IJISP) 2(3), 28-44, July 2008; https://doi.org/10.4018/jisp.2008070103https://doi.org/10.4018/jisp.2008070103http://hdl.handle.net/11603/22463The 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.17 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Text