一种基于Uniform和Influence的临床数据去标识化中的准标识符确定方案
A Determination Scheme for Quasi-Identifiers Using Uniqueness and Influence for De-Identification of Clinical Data
摘要：
目的：随着信息技术的发展，临床数据的积累和有用性增加。在使用需要可识别的临床数据以获得有意义的信息时，必须确保数据被去识别化，并最大限度地减少不必要的临床信息，以保护个人信息。这一过程需要标准和适当的方法，因为既有明确的识别标志，也有不易识别的准识别标志。方法;为了制定这样的方法，首先，通过将20个临床个人信息数据库表中的信息根据其类型分类为直接标识符（DID）、准标识符（QI）、敏感属性（SA）和非敏感属性（NSA）来选择主要准标识符。然后通过测量所选数据的唯一性值评估离群值风险，并通过计算QI去除对其他数据的影响的等效类对重新识别进行评分，从而选择次要QI。第三，对数据使用者重新识别的风险进行了数字化和分类。最后，通过将计算的重新识别分数与用户类别的阈值进行比较来确定根据用户类别的最终QI。结果;最终，通过对用户类别进行假设并将其用作标准，选择了最小值为18到最大值为28的最终质量指标。结论;当前研究人员提出的QI选择方法可供研究人员在最终检查阶段使用，然后再对选定的QI进行去识别。因此，临床数据使用者可以安全和有效地使用包含个人信息的临床数据，通过客观地选择使用本研究中提出的方法的质量指标。
英文摘要：
Objectives; The accumulation and usefulness of clinical data have increased with IT development. While using clinical data that needs to be identifiable to obtain meaningful information, it is essential to ensure that data is de-identified and unnecessary clinical information is minimized to protect personal information. This process requires criteria and an appropriate method as there are clear identifiers as well as quasi-identifiers that are not readily identifiable. Methods; To formulate such a method, first, primary quasi-identifiers were selected by classifying information in 20 clinical personal information database tables into Direct-Identifier (DID), Quasi-Identifier (QI), Sensitive Attribute (SA), and Non-Sensitive Attribute (NSA) according to its type. Secondary QIs were then selected by assessing the risk for outliers by measuring uniqueness values of the selected data and scoring re-identification by calculating equivalence class of the influence on other data on QI removal. Third, the risk of re-identification of data users was numeralized and classified. Lastly, the final QI according to user class was determined by comparing the calculated re-identification scores to the threshold values of user classes. Results; Eventually, final QIs ranging from a minimum of 18 to a maximum of 28 were selected by making an assumption about user classes and using it as criteria. Conclusions; The QI selection method presented by the current investigators can be used by researchers at the final checkup stage before they de-identify the selected QIs. Therefore, clinical data users can securely and efficiently use clinical data containing personal information by objectively selecting QIs using the method proposed in the present study.
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