TY -非盟的El伊玛目哈立德盟——Jabbouri山姆盟——地对空导弹,斯科特AU -德鲁埃Youenn盟——权力,迈克尔PY - 2006 DA - 2006/11/21 TI -评估常见De-Identification个人健康信息的启发式乔- J地中海互联网Res SP - e28六世- 8 - 4 KW -隐私KW -保密KW - HIPAA KW -安全KW -数据公开KW -伦理AB -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。伦理委员会对在观察性研究中二次使用个人健康信息的一项常见要求是，数据必须去识别。《健康保险流通与责任法案》的隐私规则、资助机构和专业协会的隐私准则以及惯例都提供了去识别的启发式方法。目的:本研究的目的是评估当遵循常见的去识别启发式方法时，由于记录链接而导致的重新识别风险是否足够低，以及风险是否在样本量和数据集上稳定。方法:采用两种方法构建识别数据集。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个个体，并对每个样本量评估所有准标识符组合的重新识别风险。在50%以上的时间里，低风险的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安大略省注册的所有医生名单和所有律师名单，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. SN - 1438-8871 UR - //www.mybigtv.com/2006/4/e28/ UR - https://doi.org/10.2196/jmir.8.4.e28 DO - 10.2196/jmir.8.4.e28 ID - info:doi/10.2196/jmir.8.4.e28 ER -