Deidentify

Deidentify refers to the process of removing or modifying personal data to prevent the identification of individuals. The goal is to reduce risks to privacy while preserving enough information for analysis, research, or sharing. Deidentification is not the same as making data truly anonymous in every circumstance; while it aims to minimize identifiability, there may still be some residual risk of re-identification, especially when data are joined with other sources.

Techniques include removing direct identifiers (names, addresses, social security numbers), and altering or suppressing indirect or quasi-identifiers (dates, ZIP codes, unique combinations). Common methods are pseudonymization (replacing identifiers with substitutes), data masking, generalization, and data aggregation. More rigorous approaches seek irreversibility or provable privacy guarantees, such as differential privacy.

Standards and frameworks provide guidance for implementing deidentification. HIPAA’s Safe Harbor method removes 18 identifiers or uses Expert Determination to assess risk. Other frameworks, such as NIST SP 800-122 and related privacy guidelines, offer risk-based approaches to deidentification and anonymization. In some jurisdictions, pseudonymization is treated as a security measure rather than full anonymization; GDPR recognizes pseudonymization as a data protection technique but does not equate it with anonymization.

Risks and limitations include the possibility of re-identification through data linkage, especially with rich or large datasets. Organizations should conduct privacy risk assessments, apply least-privilege access, and establish data use agreements to mitigate risk. Ongoing governance, documentation, and monitoring are important to maintain deidentification standards as data systems and external data sources evolve.

Applications of deidentification include healthcare research, public health reporting, and business analytics, where data utility is balanced with privacy protections to enable data sharing and analysis.