TY  - JOUR
T1  - To Reduce Data Leakage in Horizontally Distributed Database Using
Association Rules
AU - Gokulakannan, E. AU - Venkatachalapathy, K. 
JO  - Research Journal of Applied Sciences
VL  - 11
IS  - 5
SP  - 215
EP  - 220
PY  - 2016
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2016.215.220
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2016.215.220
KW  - Association rules
KW  -privacy preserving data mining
KW  -distributed database
KW  -anonymous ID assignment
KW  -cost
AB  - Data mining is used to extract important knowledge from large datasets but sometimes these datasets
are split among various parties. Association rule mining is one of the data mining technique used in distributed
databases. This technique disclose some interesting relationship between locally large and globally large item
sets and proposes an algorithm, fast distributed mining of association rules (FDM) which is an unsecured
distributed version of the Apriori algorithm used to generates a small number of candidate sets and
substantially reduces the number of messages to be passed at mining association rules. The main ingredient
in proposed protocol is two novel secure multi party algorithm-one that computes the union of private subsets
that each of the interacting player holds and another that test the inclusion of an element held by one player
in a subset held by another. This protocol offers enhanced privacy with respect to the protocol. In addition,
it is simpler and significantly more efficient in terms of communication rounds, communication cost and
computational cost.
ER  -