%0杂志文章%@ 1438-8871 %I Gunther Eysenbach %V 8% N 4% P e28% T评估个人健康信息的通用去识别启发式%A El Emam,Khaled %A Jabbouri,Sam %A Sams,Scott %A Drouet,Youenn %A Power,Michael %+ CHEO研究所，401 Smyth路，渥太华，ON K1H 8L1，加拿大，+1 613 797 5412，kelemam@uottawa.ca %K隐私%K保密%K HIPAA %K安全%K数据披露%K伦理%D 2006 %7 21.11.2006 %9原始论文%J J医学互联网Res %G英文%X背景:随着电子病历的日益普及，在观察性研究中使用这种电子临床数据的需求越来越大。伦理委员会经常要求在观察性研究中二次使用个人健康信息的数据是去识别的。去识别启发式在健康保险可携带性和责任法案隐私规则、资助机构和专业协会隐私指南和常见实践中提供。目的:本研究的目的是评估在遵循常见的去识别启发式方法时，由于记录关联引起的再识别风险是否足够低，以及风险在样本量和数据集上是否稳定。方法:采用两种方法构建鉴定数据集。在这些设备上模拟了重新识别攻击。对于每个数据集，我们将样本量减少到30人，并对每个样本量评估所有准标识符组合的重新识别风险。50%以上的低风险准标识符组合被认为是稳定的。结果:我们能够构建的识别数据集是在安大略省注册的所有医生和所有律师的名单，使用1%的抽样分数。 The quasi-identifiers of region, gender, and year of birth were found to be low risk more than 50% of the time across both data sets. The combination of gender and region was also found to be low risk more than 50% of the time. We were not able to create an identification data set for the whole population. Conclusions: Existing Canadian federal and provincial privacy laws help explain why it is difficult to create an identification data set for the whole population. That such examples of high re-identification risk exist for mainstream professions makes a strong case for not disclosing the high-risk variables and their combinations identified here. For professional subpopulations with published membership lists, many variables often needed by researchers would have to be excluded or generalized to ensure consistently low re-identification risk. Data custodians and researchers need to consider other statistical disclosure techniques for protecting privacy. %R 10.2196/jmir.8.4.e28 %U //www.mybigtv.com/2006/4/e28/ %U https://doi.org/10.2196/jmir.8.4.e28

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