Maradithaya, Sumana and Hareesha, K S (2017) Secure Model for Clustering Distributed Data. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013, 22 to 25, August 2013, SJEC, Mysore.

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Abstract

Data of similar nature are disseminated across organizations and needs to be analyzed to discover patterns and obtain relevant conclusions. While mining distributed data, disclosure of the sensitive information is a limitation that needs to be handled. This paper focuses on the construction of one such privacy preserving clustering approach that clusters, scattered data using the k-means strategy securely. The proposed approach provides maximum security of the sensitive data while modeling and also generates accurate results in comparison to the past related approaches.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	privacy preserving data mining, homomorphic property, secure multiparty computation
Subjects:	Engineering > MIT Manipal > MCA
Depositing User:	MIT Library
Date Deposited:	14 Oct 2017 10:11
Last Modified:	14 Oct 2017 10:11
URI:	http://eprints.manipal.edu/id/eprint/149824

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