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Linkage attack: Difference between revisions

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definition of linkage attack which is a method that may be used by companies to identify you
 
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tag as incomplete
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{{Incomplete|Issue 1=No references}}
A '''linkage attack''' (also known as a record linkage or de-anonymization attack) occurs when anonymized data is combined with one or more external datasets to re-identify individuals. In essence, overlap in quasi-identifying details—like ratings, timestamps, demographic traits, or behavioral patterns—can act like a fingerprint that links a supposedly anonymous record back to a real person.
A '''linkage attack''' (also known as a record linkage or de-anonymization attack) occurs when anonymized data is combined with one or more external datasets to re-identify individuals. In essence, overlap in quasi-identifying details—like ratings, timestamps, demographic traits, or behavioral patterns—can act like a fingerprint that links a supposedly anonymous record back to a real person.


For example, researchers demonstrated that by matching Netflix’s anonymized movie ratings (ratings, dates, movie IDs) with the same user’s public IMDb ratings, they could re-identify specific users from the Netflix dataset. This minor overlap in non-identifying data enabled them to connect the dots despite the lack of explicit identifiers.
For example, researchers demonstrated that by matching Netflix’s anonymized movie ratings (ratings, dates, movie IDs) with the same user’s public IMDb ratings, they could re-identify specific users from the Netflix dataset. This minor overlap in non-identifying data enabled them to connect the dots despite the lack of explicit identifiers.

Revision as of 17:08, 17 August 2025

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A linkage attack (also known as a record linkage or de-anonymization attack) occurs when anonymized data is combined with one or more external datasets to re-identify individuals. In essence, overlap in quasi-identifying details—like ratings, timestamps, demographic traits, or behavioral patterns—can act like a fingerprint that links a supposedly anonymous record back to a real person.

For example, researchers demonstrated that by matching Netflix’s anonymized movie ratings (ratings, dates, movie IDs) with the same user’s public IMDb ratings, they could re-identify specific users from the Netflix dataset. This minor overlap in non-identifying data enabled them to connect the dots despite the lack of explicit identifiers.