t-Closeness
t-Closeness is a privacy model used in data anonymization to protect sensitive information in published datasets. It extends k-anonymity and l-diversity by requiring that the distribution of sensitive attributes within each equivalence class is close to the overall distribution in the entire dataset, within a threshold t. This helps prevent attribute disclosure attacks by limiting how much an attacker can infer about sensitive values.
Developers should learn t-Closeness when working with data anonymization, privacy-preserving data publishing, or compliance with regulations like GDPR or HIPAA. It is particularly useful for healthcare, financial, or census datasets where sensitive attributes (e.g., medical conditions, salaries) must be protected while maintaining data utility for analysis. Use it to implement robust anonymization algorithms that balance privacy and data usefulness.