TY - JOUR AU - Dankar, Fida Kamal AU - El Emam, Khaled AU - Neisa, Angelica AU - Roffey, Tyson PY - 2012 DA - 2012/07/09 TI - Estimating the re-identification risk of clinical data sets JO - BMC Medical Informatics and Decision Making SP - 66 VL - 12 IS - 1 AB - De-identification is a common way to protect patient privacy when disclosing clinical data for secondary purposes, such as research. One type of attack that de-identification protects against is linking the disclosed patient data with public and semi-public registries. Uniqueness is a commonly used measure of re-identification risk under this attack. If uniqueness can be measured accurately then the risk from this kind of attack can be managed. In practice, it is often not possible to measure uniqueness directly, therefore it must be estimated. SN - 1472-6947 UR - https://doi.org/10.1186/1472-6947-12-66 DO - 10.1186/1472-6947-12-66 ID - Dankar2012 ER -