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
Jan 09, 2017 · Drawbacks to k-anonymity. K-anonymity can be susceptible to the following attacks that would allow an outsider to re-identify the anonymous records. Consider the following SQL: — If disease is the sensitive field, data anonymity can be compromised by homogeneity — or Background knowledge. SELECT PatientName, Postcode, AgeGroup, Disease, stronger notion of privacy that we call -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 homogene-ity attack and the background knowledge attack,andwe L-Diversity An equivalence class is said to have L-diversity if there are at least L “well-represented” values for the sensitive attribute. A table is said to have L-diversity if every equivalence class of the table has L-diversity [4]. Ashwin Machanavajjhala et.al [6] noticed two attacks which can take place in K-Anonymity. 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. Jan 09, 2017 · Drawbacks to k-anonymity. K-anonymity can be susceptible to the following attacks that would allow an outsider to re-identify the anonymous records. Consider the following SQL: — If disease is the sensitive field, data anonymity can be compromised by homogeneity — or Background knowledge. SELECT PatientName, Postcode, AgeGroup, Disease, stronger notion of privacy that we call -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 homogene-ity attack and the background knowledge attack,andwe stronger notion of 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 homogene-ity attack and the background knowledge attack, and we
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