kanonyms

K-anonymity is a privacy model designed to protect the confidentiality of individuals in a dataset by ensuring that each record is indistinguishable from at least k-1 other records. This model is commonly used in data anonymization techniques to prevent re-identification of individuals, which can occur when data is combined with other information sources.

The concept of k-anonymity was introduced by Latanya Sweeney in 2002. It works by generalizing or suppressing certain attributes in the dataset so that each record is indistinguishable from at least k-1 other records. For example, if a dataset has a k-anonymity of 5, then each record is identical to at least four other records, making it difficult to identify any individual record.

However, k-anonymity has its limitations. It does not protect against attribute disclosure, where sensitive information can still be inferred even if the individual cannot be identified. Additionally, k-anonymity can be compromised if the adversary has background knowledge about the individuals in the dataset.

To address these limitations, several extensions and enhancements to k-anonymity have been proposed, such as l-diversity, t-closeness, and (α,k)-anonymity. These models aim to provide stronger privacy guarantees by considering additional factors such as the diversity of sensitive attributes and the distribution of sensitive values.