Mining association rules from huge amounts of data is an important issue in data mining, with the discovered information often being commercially valuable. Moreover, companies that conduct similar business are often willing to collaborate with each other by mining significant knowledge patterns from the collaborative datasets to gain the mutual benefit. However, in a cooperative project, some of these companies may want certain strategic or private data called sensitive patterns not to be published in the database. Therefore, before the database is released for sharing, some sensitive patterns have to be hidden in the database because of privacy or security concerns. To solve this problem, sensitive-knowledge-hiding (association rules hiding) problem has been discussed in the research community working on security and knowledge discovery. The aim of these algorithms is to extract as much as nonsensitive knowledge from the collaborative databases as possible while protecting sensitive information. Sensitive-knowledge-hiding problem was proven to be a nondeterministic polynomial-time hard problem. After that, a lot of research has been completed to solve the problem. In this article, we will introduce and discuss the major categories of sensitive-knowledge-protecting methodologies. © 2011 Wiley Periodicals, Inc.