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].
(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. k-Anonymity: A popular privacy definition Complexity –k-Anonymity is NP-hard –(log k) Approximation Algorithm exists Algorithms –Incognito (use monotonicity to prune generalization lattice) –Mondrian (multidimensional partitioning) –Hilbert (convert multidimensional problem into a 1d problem) –… 7
preserving data privacy by employing assorted anonymization methods. This paper provides a discussion on several anonymity techniques designed for preserving the privacy of microdata. This research aims to highlight three of the prominent anonymization techniques used in medical field, namely k-anonymity, l-diversity, and t-closeness.
• k-anonymity prevents identity disclosure but not attribute disclosure • To solve that problem l-diversity requires that each eq. class has at least l values for each sensitive attribute • But l-diversity has some limitations • t-closeness requires that the distribution of a sensitive attribute in any eq. class is close to the 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.
(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.
M. L -diversity: privacy beyond k-anonymity, ACM Transactions on Knowledge Discovery from Data, volume 1, Issue 1, 2007.