L-diversity: Privacy beyond k-anonymity

By Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, Muthuramakrishnan Venkitasubramaniam. TKDD, 2007

296 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/004b439ff1e6a15deedc7a7c4c6685f5ceafd237

Citations in the corpus

• Differentially Private Online Learning (2011, unknown): “E ven relatively sophisticated notions such as k-anonymity [34] andl-diversity [28] have been permeated through by attacks [16] .”
• K-Automorphism: A General Framework For Privacy Preserving Network Publication (2009, unknown): “Obviously, if G is a k -automorphic network, for any vertex v in G , we cannot distinguish v from its k − 1 symmetric vertices based on any structural information.”
• DJoin: Differentially Private Join Queries over Distributed Databases (2012, unknown): “Alternatives include randomization [1], k-anonymity [34], and l-diversity [21], which are generally less restrictive but can be vulnerable to certain attacks on privacy [12] [20].”
• Sample Complexity Bounds for Differentially Private Learning (2011, unknown): “There has also been a significant amount of prior work on privacy-preserving data mining (Agrawal and Srikant, 2000; Evfimievski et al., 2003; Sweeney, 2002; Machanavajjhala et al., 2006), which spans several communities and uses privacy models other than differential privacy.”
• Discovering frequent patterns in sensitive data (2010, unknown): “For a given level of differential privacy, quantified by the parameter ǫ , we prove high-probability bounds on how far the reported patterns can be from the true top k patterns.”
• Privacy-Preserving Data Exploration in Genome-Wide Association Studies (2013, unknown): “Loukides et al. [18] suggest using well-known generalization and suppression techniques [29], but this approach is known to have inherent flaws [17, 19].”
• GUPT: privacy preserving data analysis made easy (2012, unknown): “Researchers have invented techniques that ranged from ad-hoc obfuscation of data entries (such as the removal of Personally Identifiable Information) to more sophisticated anonymization mechanisms satisfying privacy definitions like k -anonymity [25] and (cid:96) -diversity [15].”
• Robust De-anonymization of Large Sparse Datasets (2008, unknown): “They may be boolean ( e.g. , has this book been rated?)”
• Hiding the presence of individuals from shared databases (2007, unknown): “…generally ac­cepted statistical and scienti.c principles determines that the risk is very small that the information could be used, alone or in com­bination with other reasonably available information, by an antici­pated recipient to identify an individual who is a subject of the in­formation .”
• L-diversity: privacy beyond k-anonymity (2006, unknown): “[Machanavajjhala et al. 2006] first introduced l-diversity which, unlike k-anonymity, was aware of the distribution of values of the sensitive attributes and of the effects of background knowledge.”