Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute. In this paper we show that l-diversity has a number of limitations.

(ICDE'06) IEEE Computer Society; Washington, DC: 2006. l-Diversity: privacy beyond k-anonymity. pp. 24–35. Madnick SE, Lee YW, Wang RY, Zhu H. Overview and framework for data and information quality research. ACM Journal of Data and Information Quality. 2009; 1 (1) Article 2, 22. K-Anonymity Sweeny came up with a formal protection model named k-anonymity What is K-Anonymity? If the information for each person contained in the Attacks Against K‐Anonymity(Cont’d) k‐Anonymity does not provide privacy if: Sensitive values in an equivalence class lack diversity Zipcode AgeDisease A 3‐anonymous patient table The attacker has background knowledge Homogeneity Attack 476** 2* Heart Disease 476** 2* Heart Disease 476** 2* Heart Disease Bob Zipcode Age “Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression”. Technical Report SRI-CSL-98-04. Computer Science Laboratory, SRI International.

(ICDE'06) IEEE Computer Society; Washington, DC: 2006. l-Diversity: privacy beyond k-anonymity. pp. 24–35. Madnick SE, Lee YW, Wang RY, Zhu H. Overview and framework for data and information quality research. ACM Journal of Data and Information Quality. 2009; 1 (1) Article 2, 22.

Aug 14, 2019 · k-anonymity suffers with the record linkage attack (Fung et al., 2010) when there is an insufficient diversity between sensitive values in the dataset. Therefore, l-diversity (Machanavajjhala et al., 2006, Machanavajjhala et al., 2007) was proposed which is also known as privacy beyond k-anonymity. Aug 23, 2007 · Improving both k-anonymity and l-diversity requires fuzzing the data a little bit. Broadly, there are three ways you can do this: You can generalize the data to make it less specific. (E.g. the age “34” becomes “30-40”, or a diagnosis of “Chronic Cough” becomes “Respiratory Disorder” You can suppress the data. Simply delete it. Mar 12, 2019 · A data set is said to satisfy ℓ -diversity if, for each group of records sharing a combination of key attributes, there are at least ℓ “well represented” values for each confidential attribute. A table is said to have l -diversity if every equivalence class of the table has l-diversity [1, 2]. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute. In this paper we show that l-diversity has a number of limitations.

privacy that we call ℓ-diversity, the focus of this paper. But we are jumping ahead in our story. Let us first show the two attacks to give the intuition behind the problems with k-anonymity. 1.1 Attacks On k-Anonymity In this section we present two attacks, the homogeneity attack and the background

M. L -diversity: privacy beyond k-anonymity, ACM Transactions on Knowledge Discovery from Data, volume 1, Issue 1, 2007. L-diversity: privacy beyond k-anonymity - CORE Reader Achieving k-anonymity privacy protection using generalization and suppression – This is the paper introducing the concept of k-anonymity as a privacy criteria and the popular technique of data generalization. L-diversity privacy beyond k-anonymity – A more appropriate privacy measure as compared to k-anonymity. Jan 09, 2018 · The concepts of k-anonymity [46,47,48], l-diversity [47, 49, 50] and t-closeness [46, 50] have been introduced to enhance this traditional technique. k-anonymity In this technique, the higher the value of k, the lower will be the probability of re-identification. However, it may lead to distortions of data and hence greater information loss due (ICDE'06) IEEE Computer Society; Washington, DC: 2006. l-Diversity: privacy beyond k-anonymity. pp. 24–35. Madnick SE, Lee YW, Wang RY, Zhu H. Overview and framework for data and information quality research. ACM Journal of Data and Information Quality. 2009; 1 (1) Article 2, 22. K-Anonymity Sweeny came up with a formal protection model named k-anonymity What is K-Anonymity? If the information for each person contained in the