Association Rules Hiding for Privacy Preserving Data Mining: A Survey

Abstract

(PPDM) privacy preserving data mining is recent advanced research in (DM) data mining field; Many efficient and practical techniques have been proposed for hiding sensitive patterns or information from been discovered by (DM) data mining algorithms. (ARM) Association rule mining is the most important tool in (DM) data mining, that is considered a powerful and interested tool for discovering relationships between items, which are hidden in large database and may provide business competitors with an advantage, thus the hiding of association rules is the most important point in (PPDM) privacy preserving data mining for protecting sensitive and crucial data against unauthorized access; Many Practical techniques and approaches have been proposed for hiding association rules for (PPDM) privacy preserving data mining; In this paper the current existing techniques and algorithms for all approaches for (ARH) association rule hiding have been summarized.

Novel Approach to Hide Sensitive Association Rules by Introducing Transaction Affinity

“…The fifth class, cryptographic approaches, is used in multi-party computation where data is dispersed in several locations [18]. The owner may want to share their data but does not want the confidential information to be disclosed.…”