Introduction to De-Identification?
Last updated
Last updated
De-identification refers to the process of removing or modifying personally identifiable information from data in such a way that the remaining data cannot be linked back to specific individuals.
Quasi-identifiers are a set of attributes or data points that, when combined with external information or other identifiers, could potentially lead to the identification of an individual or reveal sensitive information. While quasi-identifiers themselves may not directly identify an individual, their combination and correlation with other data can pose privacy risks.
Common examples of quasi-identifiers include attributes like age, gender, ZIP code, occupation, educational background, and date of birth.
In 2000, Latanya Sweeney published a seminal paper titled “Simple Demographics Often Identify People Uniquely” in the Journal of the Massachusetts Institute of Technology. In this study, she showed that seemingly anonymous datasets containing only a few basic demographic attributes (such as ZIP code, birth date, and gender) could be combined with external information sources to re-identify individuals with a high degree of accuracy.
In the context of data privacy and de-identification, quasi-identifiers play a crucial role as they need to be carefully managed to prevent re-identification attacks.
When data is de-identified, the original identifiers, such as names, social security numbers, or other unique identifiers, are either replaced with pseudonyms or entirely removed. This transformation aims to ensure that the data no longer contains information that can be used to directly identify individuals.
By employing de-identification techniques, you can minimize the risk of data breaches, unauthorized access, and privacy violations while still being able to share, analyze, and store your data for various legitimate purposes.
