@Article{信息:doi 10.2196 / / jmir。2471，作者="Brumen, Bostjan和Heri{\v{c}}ko, Marjan和Sev{\v{c}}nikar, Andrej和Zavr{\v{s}}nik, Jernej和H{\ o}lbl, Marko"，标题="外包医疗数据分析:技术可以克服法律、隐私和保密问题吗?"，期刊="J医学互联网研究"，年="2013"，月="12"，日="16"，卷="15"，数="12"，页="e283"，关键词="保密;患者数据隐私;数据保护;医疗决策;计算机辅助;背景:医学数据是获取知识的金矿，这些知识可以改变一个病人的生命进程，甚至是整个人口的健康。数据分析师需要完全访问相关数据，但医疗数据隐私和机密性法律法规可能会拒绝完全访问，特别是当数据分析师与数据所有者没有关联时。目标:我们的第一个目标是分析与医疗数据相关的隐私和保密问题以及相关法规，并确定适当解决这些问题的技术。我们的第二个目标是制定一种程序来保护医疗数据，使外包的分析人员能够对受保护的数据进行分析，其结果即使不相同，也可以与对原始数据进行的分析相比较。 Specifically, our hypothesis was there would not be a difference between the outsourced decision trees built on encrypted data and the ones built on original data. Methods: Using formal definitions, we developed an algorithm to protect medical data for outsourced analyses. The algorithm was applied to publicly available datasets (N=30) from the medical and life sciences fields. The analyses were performed on the original and the protected datasets and the results of the analyses were compared. Bootstrapped paired t tests for 2 dependent samples were used to test whether the mean differences in size, number of leaves, and the accuracy of the original and the encrypted decision trees were significantly different. Results: The decision trees built on encrypted data were virtually the same as those built on original data. Out of 30 datasets, 100{\%} of the trees had identical accuracy. The size of a tree and the number of leaves was different only once (1/30, 3{\%}, P=.19). Conclusions: The proposed algorithm encrypts a file with plain text medical data into an encrypted file with the data protected in such a way that external data analyses are still possible. The results show that the results of analyses on original and on protected data are identical or comparably similar. The approach addresses the privacy and confidentiality issues that arise with medical data and is adherent to strict legal rules in the United States and Europe regarding the processing of the medical data. ", issn="14388871", doi="10.2196/jmir.2471", url="//www.mybigtv.com/2013/12/e283/", url="https://doi.org/10.2196/jmir.2471", url="http://www.ncbi.nlm.nih.gov/pubmed/24342053" }