Semianonymity

Semianonymity is a privacy concept describing a state in which an individual’s identity is not openly disclosed but can be partially inferred or linked under certain conditions. It sits between complete anonymity and full pseudonymity; direct identifiers may be removed, yet residual information or context can still allow identification when combined with other data sources.

In practice, semianonymous data often involves pseudonyms or handles paired with profile attributes, or data releases where names are stripped but quasi-identifiers such as birth dates or postal codes remain. Online platforms may preserve internal mappings between pseudonyms and real identities for administrative purposes, while presenting a semianonymous surface to the public. Semianonymity can also arise in datasets where linkage across records is possible through shared attributes, creating a path to re-identification.

Risks associated with semianonymity include the potential for de-anonymization through correlation with external information or triangulation across multiple data sources. The likelihood of re-identification depends on the specificity and number of quasi-identifiers, the size of the population, and the availability of auxiliary datasets. Privacy-preserving techniques such as de-identification, pseudonymization, and differential privacy aim to reduce these risks, but semianonymous data remains vulnerable under certain conditions.

Governance and practice guidance suggest treating semianonymous data with care, particularly where there is any chance of re-identification. Organizations should conduct risk assessments, limit access, implement data minimization, and apply privacy-preserving methods as appropriate. Semianonymity is frequently discussed in relation to anonymity, pseudonymity, de-identification, and re-identification.

See also: anonymity, pseudonymity, de-identification, re-identification, k-anonymity, differential privacy.