Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Evaluating re-identification risks with respect to the HIPAA privacy rule

Evaluating re-identification risks with respect to the HIPAA privacy rule AbstractObjective Many healthcare organizations follow data protection policies that specify which patient identifiers must be suppressed to share “de-identified” records. Such policies, however, are often applied without knowledge of the risk of “re-identification”. The goals of this work are: (1) to estimate re-identification risk for data sharing policies of the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule; and (2) to evaluate the risk of a specific re-identification attack using voter registration lists.Measurements We define several risk metrics: (1) expected number of re-identifications; (2) estimated proportion of a population in a group of size g or less, and (3) monetary cost per re-identification. For each US state, we estimate the risk posed to hypothetical datasets, protected by the HIPAA Safe Harbor and Limited Dataset policies by an attacker with full knowledge of patient identifiers and with limited knowledge in the form of voter registries.Results The percentage of a state's population estimated to be vulnerable to unique re-identification (ie, g=1) when protected via Safe Harbor and Limited Datasets ranges from 0.01% to 0.25% and 10% to 60%, respectively. In the voter attack, this number drops for many states, and for some states is 0%, due to the variable availability of voter registries in the real world. We also find that re-identification cost ranges from $0 to $17 000, further confirming risk variability.Conclusions This work illustrates that blanket protection policies, such as Safe Harbor, leave different organizations vulnerable to re-identification at different rates. It provides justification for locally performed re-identification risk estimates prior to sharing data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Evaluating re-identification risks with respect to the HIPAA privacy rule

Download PDF
9 pages

Loading next page...
 
/lp/oxford-university-press/evaluating-re-identification-risks-with-respect-to-the-hipaa-privacy-BIR4kkz6ZV

References (31)

Publisher
Oxford University Press
Copyright
© 2010, Published by the BMJ Publishing Group Limited For permission to use, (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1136/jamia.2009.000026
pmid
20190059
Publisher site
See Article on Publisher Site

Abstract

AbstractObjective Many healthcare organizations follow data protection policies that specify which patient identifiers must be suppressed to share “de-identified” records. Such policies, however, are often applied without knowledge of the risk of “re-identification”. The goals of this work are: (1) to estimate re-identification risk for data sharing policies of the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule; and (2) to evaluate the risk of a specific re-identification attack using voter registration lists.Measurements We define several risk metrics: (1) expected number of re-identifications; (2) estimated proportion of a population in a group of size g or less, and (3) monetary cost per re-identification. For each US state, we estimate the risk posed to hypothetical datasets, protected by the HIPAA Safe Harbor and Limited Dataset policies by an attacker with full knowledge of patient identifiers and with limited knowledge in the form of voter registries.Results The percentage of a state's population estimated to be vulnerable to unique re-identification (ie, g=1) when protected via Safe Harbor and Limited Datasets ranges from 0.01% to 0.25% and 10% to 60%, respectively. In the voter attack, this number drops for many states, and for some states is 0%, due to the variable availability of voter registries in the real world. We also find that re-identification cost ranges from $0 to $17 000, further confirming risk variability.Conclusions This work illustrates that blanket protection policies, such as Safe Harbor, leave different organizations vulnerable to re-identification at different rates. It provides justification for locally performed re-identification risk estimates prior to sharing data.

Journal

Journal of the American Medical Informatics Association – Oxford University Press

Published: Mar 1, 2010

There are no references for this article.