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Author Liu, Xiaoping ♦ Li, Xiao-Bai ♦ Motiwalla, Luvai ♦ Li, Wenjun ♦ Zheng, Hua ♦ Franklin, Patricia D.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Data sharing ♦ HIPAA ♦ Disclosure risk
Abstract Medical and health data are often collected for studying a specific disease. For such same-disease microdata, a privacy disclosure occurs as long as an individual is known to be in the microdata. Individuals in same-disease microdata are thus subject to higher disclosure risk than those in microdata with different diseases. This important problem has been overlooked in data-privacy research and practice, and no prior study has addressed this problem. In this study, we analyze the disclosure risk for the individuals in same-disease microdata and propose a new metric that is appropriate for measuring disclosure risk in this situation. An efficient algorithm is designed and implemented for anonymizing same-disease data to minimize the disclosure risk while keeping data utility as good as possible. An experimental study was conducted on real patient and population data. Experimental results show that traditional reidentification risk measures underestimate the actual disclosure risk for the individuals in same-disease microdata and demonstrate that the proposed approach is very effective in reducing the actual risk for same-disease data. This study suggests that privacy protection policy and practice for sharing medical and health data should consider not only the individuals’ identifying attributes but also the health and disease information contained in the data. It is recommended that data-sharing entities employ a statistical approach, instead of the HIPAA's Safe Harbor policy, when sharing same-disease microdata.
Description Author Affiliation: University of Massachusetts Lowell, Lowell, MA (Liu, Xiaoping; Li, Xiao-Bai; Motiwalla, Luvai); University of Massachusetts Medical School, Worcester, MA (Li, Wenjun; Zheng, Hua; Franklin, Patricia D.)
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-10-06
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 7
Issue Number 4
Page Count 14
Starting Page 1
Ending Page 14


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Source: ACM Digital Library