kanonymity

Kanonymity, commonly written as k-anonymity, is a property of a data release that aims to protect privacy by ensuring that each individual is indistinguishable from at least k−1 others with respect to a defined set of quasi-identifiers. Quasi-identifiers are attributes that may not identify a person on their own but could do so when combined with external information (for example, ZIP code, birth year, and gender).

To achieve k-anonymity, data publishers generalize or suppress quasi-identifier values until every combination of quasi-identifiers appears in at least k records. For example, a table containing ZIP, birth year, and another attribute can be transformed so that ZIP is generalized to a broader area and birth year to a decade, producing equivalence classes of size at least k. This makes reidentification of a specific individual by linking to external data more difficult.

Originating in the data privacy literature, the concept was introduced by Latanya Sweeney in 2002 as a formal approach to protecting individual privacy in microdata releases. Since then, k-anonymity has influenced practices in health data, census data, and other domains where sharing detailed person-level data is common.

Limitations of k-anonymity include vulnerability to attribute disclosure if all records within an equivalence class share the same sensitive attribute, and susceptibility to homogeneity and background knowledge attacks. Even with k-anonymity, an attacker may infer sensitive information or membership in a dataset, especially when external data sources are abundant. Extensions such as l-diversity and t-closeness, as well as broader approaches like differential privacy, address some of these weaknesses by strengthening how sensitive attributes are distributed within classes or by adding noise. Choosing an appropriate k and a suitable set of quasi-identifiers remains a key design decision. See also l-diversity, t-closeness, and differential privacy.