Journal
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Volume 73, Issue 7-8, Pages 599-606Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2004.05.002
Keywords
consumer informatics; patient privacy; confidentiality; anonymization; predictive modeling
Categories
Funding
- NLM NIH HHS [R01LM07273, R01LM06538-01] Funding Source: Medline
Ask authors/readers for more resources
One of the fundamental rights of patients is to have their privacy protected by health care organizations, so that information that can be used to identify a particular individual is not used to reveal sensitive patient data such as diagnoses, reasons for ordering tests, test results, etc. A common practice is to remove sensitive data from databases that are disseminated to the public, but this can make the disseminated database useless for important public health purposes. If the degree of anonymity of a disseminated data set could be measured, it would be possible to design algorithms that can assure that the desired level of confidentiality is achieved. Privacy protection in disseminated databases can be facititated by the use of special ambiguation algorithms. Most of these algorithms are aimed at making one individual indistinguishable from one or more of his peers. However, even in databases considered anonymous, it may still be possible to obtain sensitive information about some individuals or groups of individuals with the use of pattern recognition algorithms. In this article, we study the problem of determining the degree of ambiguation in disseminated databases and discuss its implications in the development and testing of anonymization algorithms. (C) 2004 Elsevier Ireland Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available