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Reseach Article

Association Rules Hiding for Privacy Preserving Data Mining: A Survey

by Gehad Ahmed Sultan Abd El_Aleem, Laila Abd_Ellatif, Ahmed Sharaf
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 150 - Number 12
Year of Publication: 2016
Authors: Gehad Ahmed Sultan Abd El_Aleem, Laila Abd_Ellatif, Ahmed Sharaf

Gehad Ahmed Sultan Abd El_Aleem, Laila Abd_Ellatif, Ahmed Sharaf . Association Rules Hiding for Privacy Preserving Data Mining: A Survey. International Journal of Computer Applications. 150, 12 ( Sep 2016), 34-43. DOI=10.5120/ijca2016911664

@article{ 10.5120/ijca2016911664,
author = { Gehad Ahmed Sultan Abd El_Aleem, Laila Abd_Ellatif, Ahmed Sharaf },
title = { Association Rules Hiding for Privacy Preserving Data Mining: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 150 },
number = { 12 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 34-43 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016911664 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:55:50.106224+05:30
%A Gehad Ahmed Sultan Abd El_Aleem
%A Laila Abd_Ellatif
%A Ahmed Sharaf
%T Association Rules Hiding for Privacy Preserving Data Mining: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 150
%N 12
%P 34-43
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

(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.

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Index Terms

Computer Science
Information Sciences


(DM) Data Mining (PPDM) Privacy Preserving Data Mining (ARM) Association Rules Mining (ARH) Association Rules Hiding (MST) minimum support threshold (MCT) minimum confidence threshold (SE) Side Effects and (SAR) Sensitive Association Rules .