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An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods.
MedLine Citation:
PMID:  23612074     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
Electronic Health Records (EHRs) enable the sharing of patients' medical data. Since EHRs include patients' private data, access by researchers is restricted. Therefore k-anonymity is necessary to keep patients' private data safe without damaging useful medical information. However, k-anonymity cannot prevent sensitive attribute disclosure. An alternative, l-diversity, has been proposed as a solution to this problem and is defined as: each Q-block (ie, each set of rows corresponding to the same value for identifiers) contains at least l well-represented values for each sensitive attribute. While l-diversity protects against sensitive attribute disclosure, it is limited in that it focuses only on diversifying sensitive attributes. The aim of the study is to develop a k-anonymity method that not only minimizes information loss but also achieves diversity of the sensitive attribute. This paper proposes a new privacy protection method that uses conditional entropy and mutual information. This method considers both information loss as well as diversity of sensitive attributes. Conditional entropy can measure the information loss by generalization, and mutual information is used to achieve the diversity of sensitive attributes. This method can offer appropriate Q-blocks for generalization. We used the adult database from the UCI Machine Learning Repository and found that the proposed method can greatly reduce information loss compared with a recent l-diversity study. It can also achieve the diversity of sensitive attributes by counting the number of Q-blocks that have leaks of diversity. This study provides a privacy protection method that can improve data utility and protect against sensitive attribute disclosure. The method is viable and should be of interest for further privacy protection in EHR applications.
Authors:
Sunyong Yoo; Moonshik Shin; Doheon Lee
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Publication Detail:
Type:  Journal Article     Date:  2012-11-13
Journal Detail:
Title:  Interactive journal of medical research     Volume:  1     ISSN:  1929-073X     ISO Abbreviation:  Interact J Med Res     Publication Date:  2012  
Date Detail:
Created Date:  2013-04-24     Completed Date:  2013-04-25     Revised Date:  2013-04-29    
Medline Journal Info:
Nlm Unique ID:  101598421     Medline TA:  Interact J Med Res     Country:  Canada    
Other Details:
Languages:  eng     Pagination:  e14     Citation Subset:  -    
Affiliation:
Department of Bio and Brain Engineering, KAIST, Daejeon, Korea, Republic Of.
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