Data and knowledge hiding are two research directions that investigate how the privacy of raw data, or information, can be maintained either before or after the course of mining the data. By focusing on the knowledge hiding thread, we present a taxonomy and a survey of recent approaches that have been applied to the association rule hiding problem. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. We also provide a thorough comparison of the presented approaches, and we touch upon hiding approaches used for other data mining tasks. A detailed presentation of metrics used to evaluate the performance of those approaches is also given. Finally, we conclude our study by enumerating interesting future directions in this research body.
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Verykios, V.S., Gkoulalas-Divanis, A. (2008). A Survey of Association Rule Hiding Methods for Privacy. In: Aggarwal, C.C., Yu, P.S. (eds) Privacy-Preserving Data Mining. Advances in Database Systems, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-70992-5_11
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