L-diversity: privacy beyond k-anonymity

TLDR

Publishing individual data while protecting sensitive information is a key challenge; k‑anonymity has become popular, defining privacy by making each record indistinguishable from at least k‑1 others on identifying attributes. The paper demonstrates that κ‑anonymity suffers from severe privacy flaws via two attacks and proposes ℓ‑diversity as a stronger definition. The authors analyze two attacks on κ‑anonymity and introduce ℓ‑diversity, a new privacy definition. The attacks reveal that low sensitive attribute diversity and background knowledge can compromise κ‑anonymity, and experimental evaluation shows ℓ‑diversity is practical and efficient.

Abstract

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called \kappa-anonymity has gained popularity. In a \kappa-anonymized dataset, each record is indistinguishable from at least k—1 other records with respect to certain "identifying" attributes. In this paper we show with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems. First, we show that an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. Second, attackers often have background knowledge, and we show that \kappa-anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks and we propose a novel and powerful privacy definition called \ell-diversity. In addition to building a formal foundation for \ell-diversity, we show in an experimental evaluation that \ell-diversity is practical and can be implemented efficiently.