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Privacy Preserving Association Rules Mining in Horizontally Distributed Databases Using FDM and K and C algorithm
Gayatri K. Chaturvedi,  Ranjit M.Gawande
Data mining is the most fast growing area today which is used to extract important knowledge from large data collections but often these collections are divided among several parties. This paper addresses secure mining of association rules over horizontally partitioned data. This method incorporates a protocol is that of Kantarcioglu and Clifton well known as K&C protocol. This protocol is based on an unsecured distributed version of the Apriori algorithm named as Fast Distributed Mining (FDM) algorithm of Cheung et al. The main ingredients in our protocol are two novel secure multi-party algorithms one that computes the union of private subsets that each of the interacting players hold and another that tests the whether an element held by one player is included in a subset held by another. This protocol offers enhanced privacy with respect to the earlier protocols. In addition, it is not complicated and is importantly more effectual in terms of communication cost, communication rounds and computational cost. We present a two multiparty algorithm for efficiently discovering frequent item sets with minimum support levels without either player (site) revealing it to all players.
Keywords- Security, Privacy, Data Mining, Frequent Item sets, Association Rules, multi-party
Unique Identification Number - IJEDR1403084Page Number(s) - 3334-3337Pubished in - Volume 2 | Issue 3 | Sept 2014DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Gayatri K. Chaturvedi,  Ranjit M.Gawande,   "Privacy Preserving Association Rules Mining in Horizontally Distributed Databases Using FDM and K and C algorithm"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 3, pp.3334-3337, Sept 2014, Available at :http://www.ijedr.org/papers/IJEDR1403084.pdf