L-Diversity Each equi-class has at least l well-represented sensitive values Instantiations Distinct l-diversity: Each equi-class has at least l distinct value Entropy l-diversity: Entropy(equi-class) ≥log(l) Recursive (c,l)-diversity: r 1

2. From k-Anonymity to -Diversity The protection k-anonymity provides is simple and easy to understand. If a table satisfies k-anonymity for some value k, then anyone who knows only the quasi-identifier values of one individual cannot identify the record corre-sponding to that individual with confidence grater than 1/k. To further improve privacy properties of the k-anonymity mechanism, the l-diversity concept has been introduced (Machanavajjhala et al., 2006): the cloaked region containing the k individuals must Apr 07, 2006 · L-diversity: privacy beyond k-anonymity 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. Mar 01, 2007 · In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k -anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes.

ICDE 2017 Best Paper Award: Scalable Linear Algebra on a Relational Database System, Shangyu Luo, Zekai Gao, Michael Gubanov, Christopher Jermaine and Luis Perez ICDE 2017 Influential Paper Award: L-diversity: privacy beyond k-anonymity, Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, Muthuramakrishnan Venkitasubramaniam

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 Date Paper Presenter Slides; Sep 1: Introduction and Administrivia: Apu Kapadia: Background: Privacy Sep 8: Background: Security and Cryptography Apu Kapadia

• 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

• 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.