A Globally Optimal k-Anonymity Method for the De-Identification of Health Data
نویسندگان
چکیده
منابع مشابه
Research Paper: A Globally Optimal k-Anonymity Method for the De-Identification of Health Data
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 tha...
متن کاملA Globally Optimal k-Anonymity Method for the De-Identification of Health Data
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 diff...
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Data privacy preservation has drawn considerable interests in data mining research recently. The k-anonymity model is a simple and practical approach for data privacy preservation. This paper proposes a novel clustering method for conducting the k-anonymity model effectively. In the proposed clustering method, feature weights are automatically adjusted so that the information distortion can be ...
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Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relat...
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ژورنال
عنوان ژورنال: Journal of the American Medical Informatics Association
سال: 2009
ISSN: 1067-5027,1527-974X
DOI: 10.1197/jamia.m3144