Quasi-identifiers are pieces of information that are not of themselves unique identifiers, but are sufficiently well correlated with an entity that they can be combined with other quasi-identifiers to create a unique identifier.
As an example, Latanya Sweeney has shown that even though neither gender, birth dates nor postal codes uniquely identify an individual, the combination of all three is sufficient to identify 87% of individuals in the United States.
For instance, Sweeney linked health records to publicly available information to locate the then-governor of Massachusetts' hospital records using uniquely identifying quasi-identifiers,[4][5] and Sweeney, Abu and Winn used public voter records to re-identify participants in the Personal Genome Project.
[6] Additionally, Arvind Narayanan and Vitaly Shmatikov discussed on quasi-identifiers to indicate statistical conditions for de-anonymizing data released by Netflix.
[7] Motwani and Ying warn about potential privacy breaches being enabled by publication of large volumes of government and business data containing quasi-identifiers.