Title

Preserving Patient Privacy When Sharing Same-Disease Data

UMMS Affiliation

Department of Orthopedics and Physical Rehabilitation; Department of Medicine, Division of Preventive and Behavioral Medicine; UMass Worcester Prevention Research Center

Publication Date

10-1-2016

Document Type

Article

Disciplines

Bioethics and Medical Ethics | Biostatistics | Health Information Technology | Health Policy | Public Health

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.

Keywords

Data sharing, HIPAA, disclosure risk

DOI of Published Version

10.1145/2956554

Source

ACM J Data Inf Qual. 2016 Oct;7(4). pii: 17. Link to article on publisher's site

Journal/Book/Conference Title

ACM journal of data and information quality

Related Resources

Link to Article in PubMed

PubMed ID

27867450

Link to Full Text

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