Quasi-identifier

Quasi-identifiers are pieces of information that are not of themselves <a href="/facts/Unique_identifier/NMcgc7qX">unique identifiers</a>, but are sufficiently well correlated with an entity that they can be combined with other quasi-identifiers to create a unique identifier.
Quasi-identifiers can thus, when combined, become <a href="/facts/Personally_identifying_information/384PbBg5">personally identifying information</a>. This process is called <a href="/facts/Re-identification/vPn56Dso">re-identification</a>. As an example, <a href="/facts/Latanya_Sweeney/k80bUyxn">Latanya Sweeney</a> has shown that even though neither <a href="/facts/Gender/LfjRHcJy">gender</a>, <a href="/facts/Birth_date/CNhXkRxb">birth dates</a> nor <a href="/facts/Postal_code/5Kckdr9M">postal codes</a> uniquely identify an individual, the combination of all three is sufficient to identify 87% of individuals in the United States. 
The term was introduced by Tore Dalenius in 1986. Since then, quasi-identifiers have been the basis of several attacks on released data. For instance, Sweeney linked health records to publicly available information to locate the then-governor of Massachusetts' hospital records using uniquely identifying quasi-identifiers, and Sweeney, Abu and Winn used public voter records to re-identify participants in the Personal Genome Project. Additionally, Arvind Narayanan and <a href="/facts/Vitaly_Shmatikov/jdV3LM4f">Vitaly Shmatikov</a> discussed on quasi-identifiers to indicate statistical conditions for de-anonymizing data released by Netflix.
Motwani and Ying warn about potential privacy breaches being enabled by publication of large volumes of government and business data containing quasi-identifiers.

Quasi-identifier open-in-new

Quasi-identifier