Data De-identification

Data de-identification is the process of modifying or removing personally identifiable information from datasets in a way that minimizes the risk of unintended disclosure of individuals' identities. The paper defines terms such as anonymization, blurring, de-identification, disclosure, disclosure avoidance, and various methods like masking, perturbation, redaction, and suppression. It highlights the significance of evaluating disclosure limitation techniques and provides an alphabetized glossary of frequently used terms. The document also discusses the release of de-identified data under FERPA and includes additional resources for understanding and implementing data de-identification methods.