Privacy-preserving anonymization of set-valued data

By Manolis Terrovitis, Nikos Mamoulis, Panos Kalnis. Proceedings of the VLDB Endowment, 2008

356 citations recorded by the source corpus.

How researchers see this paper

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Trustworthy AI: From Principles to Practices (2021, background): “Specific DA methods have also been designed for relational data [262], set-valued data [151, 320], and image data [97, 239].”
A Survey on Differential Privacy for Unstructured Data Content (2022, background): “A rich set of obfuscation methods have been proposed to protect unstructured data [38, 74, 86, 96].”
PrivBasis: Frequent Itemset Mining with Differential Privacy (2012, methodology): “He and Naughton [24] enhance [32] by strictly imposing kanonymity.”
Slicing: A New Approach to Privacy Preserving Data Publishing (2009, methodology): “For example, slicing can be used for anonymizing transaction databases, which has been studied recently in [32, 37, 26].”