Association rule hiding

By Vassilios S. Verykios, Ahmed K. Elmagarmid, Elisa Bertino, Yücel Saygin, Elena Dasseni. IEEE Transactions on Knowledge and Data Engineering, 2004

583 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/bb24f265ad1166f9b992561d165495c5c8b1d2ea

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In Artificial Intelligence

Citations in the corpus

State-of-the-art in privacy preserving data mining (2004, background): “A complete work which was based on this idea, can be found in [24].”
Publishing Search Logs—A Comparative Study of Privacy Guarantees (2009, background): “Existing work on publishing frequent itemsets often only tries to achieve anonymity or makes strong assumptions about the background knowledge of an attacker [30, 26, 34, 35, 38, 2, 39, 37].”
Privacy-Preserving Data Mining: Models and Algorithms (2008, background): “This problem is referred to as association rule hiding [17] by the database community, and that of c ntingency table privacy-preservationby the statistical community.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, methodology): “A complete description of the various methods for data distortion for association rule hiding may be found in [124].”
Mining Association Rules under Privacy Constraints (2008, background): “A detailed treatment of this issue is presented in [44].”
Privacy Preserving Data Mining Research: Current Status and Key Issues (2007, background): “In a centralized database (C-DB) environment, data are all stored in a single database; while, in a distributed database (D-DB) environment, data are stored in different databases [3]-[6] [13]-[22] [27] [29] [31] .”
A rigorous and customizable framework for privacy (2012, background): “, [9, 7, 31, 38, 37, 28, 8, 2, 39, 10, 36, 29]) that are deemed to be sensitive.”
Pufferfish (2014, background): “…rules, frequent itemsets, and classiﬁcation rules (e.g., [Clifton and Marks 1996; Clifton 2000; Oliveira and Zaiane 2003; Wang and Lee 2008; Verykios et al. 2004a, 2004b; Moustakides and Verykios 2008; Clifton et al. 2002; Aggarwal et al. 2006; Wang et al. 2005; Delis et al. 2010;…”
Discrimination prevention in data mining for intrusion and crime detection (2011, background): “It is worth mentioning that decreasing the confidence of special rules (sensitive rules) by data transformation was previously used for knowledge hiding [13], [14], [15] in privacy-preserving data mining (PPDM).”
Privacy-preserving anonymization of set-valued data (2008, background): “[20] consider a dataset D of transactions, each of which contains a number of items.”
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining (2006, methodology): “For example, the perturbation-based association rule hiding technique [26] is implemented by changing a selected set of 1-values to 0-values or vice versa so that the frequent item sets that generate the rule are hidden or the support of sensitive rules is lowered to a user-specified threshold.”
Data mining for discrimination discovery (2010, methodology): “The first one is to adapt the preprocessing approaches of data sanitization [Hintoglu et al. 2005; Verykios et al. 2004] and hierarchy-based generalization [Sweeney 2002; Wang et al.”