Call For Paper Volume:7 Issue:9 Sep'2020 |

Privacy-Preserving Outsourced Association Rule Mining using apriori algorithm.

Publication Date : 02/02/2017

DOI : 10.21884/IJMTER.2017.4038.UX0SU

Author(s) :

Twinkle Patil , Prof.K.C.Kulkarni , Pooja Somwanshi , Rohini Sonawane , Diksha Siksure.

Volume/Issue :
Volume 4
Issue 1
(02 - 2017)

Abstract :

Association rule mining and frequent item set mining are two popular and widely studied data analysis techniques for a range of applications. We focus on privacy preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent item sets from a collective data set, and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient homomorphic encryption scheme and a secure comparison scheme. We then propose a cloud-aided frequent item set mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. Keywords—Association rule mining, frequent itemset mining, privacy-preserving data mining

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Privacy-Preserving Outsourced Association Rule Mining using apriori algorithm.

January 31, 2017