De-Identifying Government Datasets: Techniques and Governance

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. This report outlines the fundamentals of data de-identification within the educational privacy framework governed by the Family Educational Rights and Privacy Act (FERPA). It introduces essential concepts like anonymization, blurring, and de-identification, emphasizing their role in safeguarding personally identifiable information (PII) from unauthorized disclosure. Additionally, it delves into the release of de-identified data under FERPA regulations and offers supplementary resources for a comprehensive understanding of data de-identification methods.