A globally optimal k-anonymity method for the de-identification of health data. El Emam, K., Dankar, F. K., Issa, R., Jonker, E., Amyot, D., Cogo, E., Corriveau, J., Walker, M., Chowdhury, S., Vaillancourt, R., Roffey, T., & Bottomley, J. Journal of the American Medical Informatics Association : JAMIA, 16(5):670--82, 2009. 00000 ![A globally optimal k-anonymity method for the de-identification of health data. [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex BACKGROUND: Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified. OBJECTIVE: The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets. DESIGN: Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. Measurement Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated. RESULTS: The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution. CONCLUSIONS: For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.
@article{el_emam_globally_2009,
title = {A globally optimal k-anonymity method for the de-identification of health data.},
volume = {16},
issn = {1527-974X},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2744718&tool=pmcentrez&rendertype=abstract},
doi = {10.1197/jamia.M3144},
abstract = {BACKGROUND: Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified. OBJECTIVE: The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets. DESIGN: Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. Measurement Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated. RESULTS: The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution. CONCLUSIONS: For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.},
number = {5},
journal = {Journal of the American Medical Informatics Association : JAMIA},
author = {El Emam, Khaled and Dankar, Fida Kamal and Issa, Romeo and Jonker, Elizabeth and Amyot, Daniel and Cogo, Elise and Corriveau, Jean-Pierre and Walker, Mark and Chowdhury, Sadrul and Vaillancourt, Regis and Roffey, Tyson and Bottomley, Jim},
year = {2009},
pmid = {19567795},
note = {00000 },
keywords = {Adolescent, Adult, Algorithms, Computerized, Confidentiality, Female, Humans, Information Storage and Retrieval, Male, Medical Records Systems},
pages = {670--82}
}
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OBJECTIVE: The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets. DESIGN: Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. Measurement Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated. RESULTS: The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution. 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