Abstract
Clustering is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. The goal is to create clusters that are coherent internally, but substantially different from each other. Privacy is an important factor while datasets or data integrates from different data holders for mining over a distributed networks. Secured and optimal data clustering in distributed networks has played an important role in many fields like Information Retrieval, Data mining, Knowledge and Data engineering or community based clustering. Secured mining of data is required in open network. In this paper we are proposing an efficient privacy preserving and optimal data clustering technique over distributed networks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Giannotti, F., Lakshman, L.V.S., Monreale, A., Pedreschi, D., Wang, H.(W.): Privacy -Preserving Mining of Association Rules From Outsourced Transaction Databases. IEEE Systems Journal 7(3) (September 2013)
Fong, P.K., Weber-Jahnke, J.H.: Privacy Preserving Decision Tree Learning Using Unrealized Data Sets. IEEE Transaction on Knowlegde And Data Engineering 24(2) (Feburary (2012)
Tassa, T., Cohen, D.J.: Anonymization of Centralized and Distributed Social Networks by Sequential Clustering. IEEE Transactions on Knowlegde and Data Engineering 25(2) (Februrary (2013)
Privacy Preserving Clustering, siis.cse.psu.edu/pubs/esorics05.pdf
Clifton, C., Kantarcioglu, M., Lin, X., Zhu, M.Y.: Tools for Privacy Preserving Distributed Data Mining. Acm Sigkdd Exploration Newsletters 4(2) (December 2002)
Datta, S., Giannella, C.R., Kargupta, H.: Approximation Distributed K-Means Clustering over a peer-to-peer network. IEEE TKDE 21(10), 1372–1388 (2009)
Eisenhardt, M., Muller, W., Henrich, A.: Classifying document by distributed P2P clustering. In: INFORMATIK (2003)
Hammouda, K.M., Kamel, M.S.: Hierarchically distributed peer-to-peer document clustering and cluster summarization. IEEE Transaction Knowledge Data Engineering 21(5), 681–698 (2009)
Hsiao, H.C., King, C.T.: Similarity discovery in structured P2P overlays. In: ICPP (2003)
Oliveria, S.R.M., Zaiane, O.R.: Privacy Preservering Clustering by Data Transformation. JIDM 1(1), 37–52 (2010)
Triple Data Encryption Standard, http://en.wikipedia.org/wiki/Triple_DES
Triple DES algorithm, http://www.vocal.com/cryptography/tdes/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bala, M.Y., Jayaprada, S. (2015). A Secure and Optimal Data Clustering Technique over Distributed Networks. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-13728-5_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13727-8
Online ISBN: 978-3-319-13728-5
eBook Packages: EngineeringEngineering (R0)