Description: Linkage attack is a technique used to re-identify anonymized data by combining different sources of information. This type of attack is based on the premise that, although individual data may not contain identifiable information, the correlation between multiple datasets can reveal the identity of individuals. For example, if one dataset contains information about a person’s age and postal code, and another dataset includes information about occupation and marital status, an attacker could cross-reference these data to identify a specific person. This phenomenon highlights the vulnerability of traditional anonymization methods, which often do not account for the possibility that data may be combined with other sources. The relevance of linkage attacks has grown in the Big Data era, where the availability of large volumes of data and the ability to process them have facilitated re-identification. Protecting data privacy has become a critical challenge, and linkage attacks serve as a reminder that anonymization is not foolproof. Therefore, it is essential to implement more robust security measures and consider the context in which data is used to mitigate the risk of re-identification.
