Désidentification

Désidentification, or de-identification, is the practice of removing or masking personal identifiers from data so that individuals cannot be readily identified. The goal is to enable data sharing and analysis while protecting privacy.

It is distinct from anonymization and pseudonymization. Anonymization removes identifiers in a way that individuals cannot be re-identified, even with auxiliary information. Pseudonymization replaces identifiers with pseudonyms, allowing re-identification only with additional information. Désidentification often aims to reduce re-identification risk while preserving data utility; the possibility of re-identification may remain if the transformation is reversible or if combined with other data sources.

Methods used in désidentification include direct identifier removal (names, addresses, social security numbers), generalization or suppression of quasi-identifiers (birth dates, postal codes), masking, hashing or tokenization, and, in some cases, the use of synthetic data. Data protection standards such as HIPAA’s Safe Harbor method and Expert Determination provide frameworks for de-identification in healthcare.

Applications span health research, public health surveillance, government statistics, and corporate data sharing. Risks include re-identification through linkage with other data sources or advances in data analytics; therefore désidentification is typically accompanied by governance structures, risk assessments, access controls, and data-use agreements.

Legally, de-identified data is treated as posing lower privacy risk under many regulations, though the exact status varies by jurisdiction. The objective is to balance privacy protection with data utility while maintaining accountability and appropriate consent where applicable.