Abstract
Data mining is a method through which we can search for a large pattern in huge database system. Now with the increasing growth of technology, the data requirements and amount of data will drastically increasing. Therefore, the data mining uses new methods for pattern matching which can be used for decision making. The organizations are stores data in bulk. Therefore, when a particular query is given by user, the amount of important or secure data can also be revealed as an answer of a query. This can harm to reputation of an organization. Therefore, privacy can concern to the above issue that not reveals any such kind of information about data provider and vice versa. Therefore, data needs to be modified without losing the data integrity. This paper outlines a method that achieve confidentiality from client and owner side which relatively less size of cipher text through mediator.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Privacy Preserving Data Mining. In: The Proceedings of the ACM SIGMOD Conference (2000)
Muralidhar, K., Sarathi, R.: A General additive data perturbation method for data base security. Journal of Management Science 45(10), 1399–1415 (2002)
Agrawal, D., Aggarwal, C.C.: On the Design and Quantification of Privacy Preserving Data mining algorithms. In: ACM PODS Conference (2002)
Muralidhar, K., Sarathy, R.: Data Shuffling- a new masking approach for numerical data. Management Science (forthcoming 2006)
Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proc. of SIGKDD 2002, Edmonton, Alberta, Canada (2002)
Lindell, Y., Pinkas, B.: Privacy Preserving Data Mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)
Yu, H., Vaidya, J., Jiang, X.: Privacy-Preserving SVM Classification on Vertically Partitioned Data. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 647–656. Springer, Heidelberg (2006)
Agarwal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the 20th Symposium on Principles of Database Systems, Santabarbara, California, USA (May 2001)
Karandikar, P., Deshpande, S.: Preserving Privacy in Data Mining using Data Distortion Approach. International Journal of Computer Engineering Science 1(2) (2011) ISSN: 2250-3439
Ravi Kumar, G., Ramachandra, G.A., Sunitha, G.: An Evolutionary Algorithm for Mining Association Rules Using Boolean Approach. International Journal of Computer Engineering Science 1(3) (2011) ISSN: 2250-3439
Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. In: The ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD 2002), Madison, pp. 24–31 (June 2002)
Muralidhar, K., Sarathy, R., Parsa, R.A.: A general additive perturbation method for database security. Management Science 45(10), 1399–1415 (1999)
Agrawal, R., Evfimievski, A., Srikant, R.: Information sharing across private databases. In: Proc. of ACM SIGMOD (2003)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers (2000)
Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: Proc. of 3rd IEEE Int. Conf. on Data Mining, Washington, DC, USA, pp. 99–106 (2003)
Muralidhar, K., Parsa, R., Sarathy, R.: A general additive data perturbation method for database security. Management Science 19, 1399–1415 (1999)
Pinkas, B.: Cryptographic techniques for privacy preserving data mining. SIGKDD Explorations, 12–19 (2002)
Evfimievski, A.: Randomization in privacy preserving data mining. ACM SIGKDD Explorations Newsletter 4, 43–48 (2002)
Vaidya, J., Clifton, C.: Privacy-Preserving Data Mining: Why, How and When. IEEE Security and Privacy 2, 19–27 (2004)
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for privacy preserving distributed data mining. SIGKDD Explor. News., 28–34 (2002)
Weiss, G.M.: Data Mining in Telecommunications. In: Data Mining and Knowledge Discovery Handbook, A Complete Guide for Practitioners and Researchers, pp. 1189–1201. Kluwer Academic Publishers (2005)
Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, Florida, November 19-22, pp. 99–106 (2003)
Muralidhar, K., Parsa, R., Sarathy, R.: A General Additive Data Perturbation Method for database Security. Management, 1399–1415 (1999)
Liu, L., Kantarcioglu, M., Thuraisingham, B.: The applicability of the perturbation based privacypreserving data mining for real-world data. Data & Knowledge Engineering 65, 5–21 (2008)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery Data Mining (July 2002)
Lindell, Y., Pinkas, B.: Privacy Preserving Data Mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)
Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26 (2002)
Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: Proc. of Intl. Conf. on Data Mining, ICDM (2003)
Kamakshi, P., VinayaBabu, A.: Preserving Privacy and Sharing the Data in Distributed Environment using Cryptographic Technique on Perturbed data. Journal of Computing 2(4) (April 2010) ISSN 2151-9617
Agarwal, R., Srikant, R.: Privacy preserving data mining. In: Procseedings of the 19th ACM SIGMOD Conference on Management of Data, Dallas, Texas, USA (May 2000)
Canny, J.: Collaborative filtering with privacy. In: IEEE Symposium on Security and Privacy, Oakland, pp. 45–57 (May 2002)
Jothimani, K., Antony SelvadossThanmani, S.: MS: Multiple Segments with Combinatorial Approach for Mining Frequent Item sets Over Data Streams. International Journal of Computer Engineering Science 2(2) (2012) ISSN : 2250-3439
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goswami, N., Chauhan, T., Doshi, N. (2012). Efficient Cryptography Technique on Perturbed Data in Distributed Environment. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31513-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-31513-8_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31512-1
Online ISBN: 978-3-642-31513-8
eBook Packages: EngineeringEngineering (R0)