Privacy preserving visualization using CLUSTERING

  IJCOT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© - June Issue 2013 by IJCTT Journal
Volume-4 Issue-6                           
Year of Publication : 2013
Authors :Sabbisetti Naga Anisha, G. Rama Krishna

MLA

Sabbisetti Naga Anisha, G. Rama Krishna "Privacy preserving visualization using CLUSTERING"International Journal of Computer Trends and Technology (IJCTT),V4(6):1632-1634 June Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract: -Today privacy preserving plays a major role in maintaining databases. Because there are many privacy breaching techniques have been developed to breach privacy and view the secured data. To overcome this drawback many techniques like k-anonymity and l-diversity are developed but they did not completely provide privacy and repeated attacks on the database. Hence visualization techniques are developed which provide privacy while displaying the information to the user. Different visualization techniques like screen space metrics are developed. Later parallel coordinates have become more popular visualization technique. In this paper we will discuss the disadvantages of the parallel coordinates and suggested methods to overcome them and proposed algorithm to this problem

 

References-
[1] R. Agrawal and R. Srikant.Privacy-preserving data mining. ACM Sigmod Record, 29(2):439–450, 2000.
[2]AritraDasgupta and Robert Kosara,Adaptive Privacy-Preserving Visualization Using Parallel Coordinates, IEEE Transactions 2011.
[3]Ting Wang Georgia Institute of Technology and Ling Liu Georgia Institute of Technology,Output Privacy in Data Mining,ACM Transactions on Database Systems, Vol. , No. , 20.
[4] C. Dwork. Differential privacy. In ICALP, pages 1–12. Springer, 2006.
[5] Ying-Huey Fua, Matthew O. Ward and Elke A. Rundensteiner,Hierarchical Parallel Coordinates for Exploration of Large Datasets, NSF grant IIS-9732897.
[6] L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty,Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570.
[7]Mahir Can Doganay, B. Pedersen,Distributed Privacy Preserving k-Means Clustering withAdditive Secret Sharing, Information SocietyTechnologies Programme of the European Commission,Future and Emerging Technologies under IST-014915 GeoPKDDproject.
[8] ManeeshUpmanyu, Anoop M. Namboodiri, KannanSrinathan, and C.V. Jawaha, Efficient Privacy Preserving K-Means Clustering, Springer-Verlag Berlin Heidelberg 2010.
[9] P. MayilVel Kumar, M.Karthikeyan, L Diversity on K-Anonymity with External Database for improving Privacy Preserving Data Publishing,International Journal of Computer Applications (0975 – 8887)
[10]GeethaJagannathan_ Krishnan Pillaipakkamnatt† Rebecca N. Wright, A New Privacy-Preserving Distributed k-Clustering Algorithm, International Conference on Data Mining (SDM), 2006

Keywords — Parallel Coordinates, visualization.