quasiidentifier

Quasiidentifier is a data attribute or set of attributes that does not uniquely identify an individual by itself but, when combined with other information, can be used to re-identify someone. In data privacy, quasi-identifiers are central to assessing disclosure risk in released datasets.

In many populations, attributes such as date of birth, ZIP code, and gender may be harmless alone but, together, narrow the set of possible individuals. The precise set that constitutes a quasi-identifier depends on context and population size.

In k-anonymity, data are transformed so that every record is indistinguishable from at least k-1 others with respect to the quasi-identifiers. This reduces re-identification risk but may degrade data utility. Additional models such as l-diversity and t-closeness address attribute disclosure within those groups.

Common techniques include generalization (reducing precision), suppression, masking, data swapping, or noise addition. Assessing quasi-identifier risk involves estimating how easily an attacker could link records to individuals using external data. Regulatory contexts: HIPAA uses de-identification standards that remove certain direct identifiers; quasi-identifiers may still pose risk if not mitigated.