  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.
