Secure Distributed Data Mining

  Suresh Gaikwad,  Hemant Kumar Gupta

Handling large dataset is a tedious task. The solution to the present downside is to use parallel or distributed approaches. Through mining, attention-grabbing relations and patterns between variables of enormous information is determined firmly victimization science techniques and therefore the mining algorithms. This paper addresses the matter of secure distributed association rule mining over the horizontally distributed information. Security is that the main downside in association rule mining comes. The performance of data mining algorithm can be accelerated from O(N) to O(N/k) with parallelism, where N = number of data records and k =number of nodes in distributed system [2]. There are several sites in the transaction. This system is predicated on distributed mining algorithmic program, K&C algorithmic program and AES algorithmic program. Distributed mining algorithm proposed here is the distributed version of apriori algorithm. The science technique is employed to produce security so as to reduce the data shared in mining. With projected technique speed up is nonheritable whereas protective the privacy of the info.

Keywords- AES; K&C; Apriori algorithm;

Unique Identification Number - IJEDR1902074
Page Number(s) - 397-401
Pubished in - Volume 7 | Issue 2 | June 2019
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Suresh Gaikwad,  Hemant Kumar Gupta,   "Secure Distributed Data Mining", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.7, Issue 2, pp.397-401, June 2019, Available at :http://www.ijedr.org/papers/IJEDR1902074.pdf