Secure Model for Clustering Distributed Data

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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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

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