Description: Quasi-identifiers are attributes that, when combined, can potentially identify an individual within a dataset. Unlike direct identifiers, such as name or identification number, quasi-identifiers are characteristics that, on their own, are not sufficient to identify a person, but when combined with other attributes, can lead to identification. Examples of quasi-identifiers include date of birth, postal code, and gender. These attributes are particularly relevant in the context of data anonymization, as their presence in a dataset can compromise individuals’ privacy. Identifying quasi-identifiers is crucial for implementing effective anonymization techniques, such as generalization and suppression, which aim to protect individuals’ identities while allowing data analysis. Proper management of quasi-identifiers is essential to comply with privacy regulations, such as GDPR, which requires that personal data be handled in a way that minimizes the risk of unauthorized identification.
History: The concept of quasi-identifiers gained popularity in the field of data privacy in the late 1990s and early 2000s, particularly with the work of Latanya Sweeney, who demonstrated how anonymous records could be re-identified using these attributes. Her research on the re-identification of medical data in 1997 was pivotal in highlighting the vulnerability of seemingly anonymous data. Since then, the study of quasi-identifiers has evolved, driving the development of more sophisticated anonymization techniques.
Uses: Quasi-identifiers are primarily used in the field of data protection and privacy. They are essential for implementing anonymization techniques that aim to protect individuals’ identities in datasets. Additionally, they are used in assessing re-identification risks in databases, helping organizations comply with privacy regulations and manage sensitive information appropriately.
Examples: A practical example of quasi-identifiers can be seen in a patient dataset that includes attributes such as date of birth, postal code, and gender. While none of these attributes can identify a patient on their own, the combination of them can allow an attacker to identify a specific individual. Another case is the use of quasi-identifiers in survey databases, where the combination of responses can reveal the identity of respondents.
