Abstract
Mankind has achieved an impressive ability to store data. The capacity of digital data storage has doubled every nine months for at least a decade. Furthermore, our skills and interest to collect data and analyze them are also remarkable. There has been a wide variety of research going on in the field of privacy preservation in data mining. Most of the methods are implemented for static data. But the world is filled with dynamic data which grows rapidly than what we expect. No technique is better than the other ones with respect to all criteria. This paper focus on privacy criteria that provide formal safety guarantees, present algorithms that sanitize data to make it safe for release while preserving useful information, and discuss ways of analyzing the sanitized data. This paper focus on a methodology that is well suited for incremental data that preserves its privacy while also performing an efficient mining .The method does not require the entire data to be processed again for the insertion of new data. The method uses frequency discretization technique that represents the interestingness of items in a database as a pattern. This method is suggested for both incremental data and providing privacy for such data. We develop the algorithm for making the database flexible in terms of mining and cost effective in terms of storage.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gitanjali, J., Indumathi, J., Sriman Narayana Iyengar, N.C.: A Pristine Clean Cabalistic Foruity Strategize Based Approach for Incremental Data Stream Privacy Preserving Data Mining. In: 2010 IEEE 2nd International Advance Computing Conference, pp. 410–415 (2010)
Rasheed, F., Lee, Y.-K., Lee, S.: In: Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE (2006)
Patra, B.K., Nandi, S., Viswanath, P.: Data summarization based fast hierarchical clustering method for large datasets. In: 2009 International Conference on Information Management and Engineering, pp. 278–282 (2009)
Yang, C.-H., Yang, D.-L.: IMBT - A Binary Tree for Efficient Support Counting of Incremental Data Mining. In: International Conference on Computational Science and Engineering (2009)
Bradley, P.S., Fayyad, U.M., Reina, C.: Scaling clustering algorithms to large databases. In: KDD, pp. 9–15 (1998)
Altiparmak, F., Ferhatosmanoglu, H.: Incremental Maintenance of Online Summaries over Multiple Streams. IEEE Transactions on Knowledge and Data Engineering 20(2) (February 2008)
Dai, B.-R., Chiang, L.-H.: IEEE Hiding Frequent Patterns in the Updated Database (2010)
Yubo, J., Yuntao, D., Yongli, W.: An Incremental Updating Algorithm for Online Mining Association Rules. In: 2009 International Conference on Web Information Systems and Mining (2009)
Jin, H., Leung, K.-S.: Scalable Model-Based Clustering for Large Databases Based on Data Summarization. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11) (November 2005)
Han, S., Ng, W.K., Wan, L.: Privacy-Preserving Gradient Descent Methods. IEEE Transactions on Knowledge and Data Engineering (March 2010)
Wu, X., Wu, G.-Q., Xie, F., Zhu, Z., Hu, X.-G., Lu, H., Li, H.: News Filtering and Summarization on the Web Intelligent Systems, vol. 25(5). IEEE (September-October 2010)
Bulut, A., Singh, A.K.: SWAT: hierarchical stream summarization in large networks. In: Proceedings of 19th International Conference on Data Engineering (March 2003)
Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 245–250 (2001)
Vaidya, J., Clifton, C.: Privacy-Preserving Association Rule Mining in Vertically Partitioned Data. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (2002)
Agrawal, R., Srikant, R.: In: Proceedings of the ACM SIGMOD Conference on Management of Data, Dallas, TX, pp. 439–450 (May 2000)
Bu, S., Lakshmanan, L., Ng, R., Ramesh, G.: Preservation of Patterns and Input-Output Privacy. In: Proc. IEEE 23rd Int’l Conf. Data Eng., pp. 696–705 (April 2007)
Aggarwal, C., Yu, P.: Privacy-Preserving Data Mining: Models and Algorithms. Springer (2008)
Fong, P.K., Weber-Jahnke, J.H.: Privacy Preserving Decision Tree Learning Using Unrealized Data Sets. IEEE Transactions on Knowledge and Data Engineering 24(2) (February 2012)
Agrawal, D., Aggarwal, C.: On the Design and Quantification of Privacy- Preserving Data Mining Algorithms. In: ACM PODS Conference (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rajalakshmi, V., Anandha Mala, G.S. (2013). An Intensified Approach for Privacy Preservation in Incremental Data Mining. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31600-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-31600-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31599-2
Online ISBN: 978-3-642-31600-5
eBook Packages: EngineeringEngineering (R0)