Anonymization is the process of removing or altering data so people are not directly identifiable. In cybersecurity, it matters because data that looks harmless can still expose identities through indirect clues such as location, timing, rare combinations of attributes, or links to other datasets. If those links remain possible, the data is only de-identified, not truly anonymous.
Attackers use re-identification techniques to connect “anonymous” records back to real people, especially when auxiliary information is available. Defenders use anonymization in data sharing, analytics, and regulatory reporting to reduce privacy risk, but they must test whether the result is still unlinkable in practice. For sensitive data like search queries, anonymization should be measured by re-identification risk, not by whether names or obvious identifiers were removed.