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Association rule hiding methods
Author(s) -
Verykios Vassilios S.
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1082
Subject(s) - association rule learning , computer science , sort , association (psychology) , data mining , presentation (obstetrics) , data science , information hiding , table (database) , information retrieval , artificial intelligence , psychology , image (mathematics) , medicine , radiology , psychotherapist
Privacy preserving data mining has been recently introduced to cope with privacy issues related to the data subjects in the course of the mining of the data. It has also been recognized that it is not only the data that need to be protected but sensitive knowledge hidden in the data as well. Knowledge hiding is an emerging area of research focusing on appropriately modifying the data in such a way that sensitive knowledge escapes the mining and is not communicated to the public for privacy purposes. This article investigates the development of techniques falling under the knowledge‐hiding umbrella that pertain to the association rule‐mining task. These techniques are known as association rule hiding or frequent pattern hiding approaches, and have been receiving a lot of attention lately because they touch upon important issues of handling a sort of commonly used patterns such as the frequent patterns and the association rules. We present an overview of this area as well as a taxonomy and a presentation of an important sample of algorithms. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules