Privacy Preserving Decision Tree Mining based on Mixed Attributes
Privacy preservation is an important concept in the field of data mining approach to introduce the protection of dataset. Our work is to secure the datasets that are utilized for the data mining and mainly focuses on conversion of original datasets into unrealized datasets. During the conversion of unrealized datasets if any leakage occurs it should be recovered by cryptographic technique i.e., Advanced Encryption Standard (AES) may be identified to convert the unrealized datasets into unreadable format through encryption. Sample datasets are taken and stored for reuse also and recover the utility of original datasets data reconstruction is performed using. The proposed Fuzzy Decision Tree (FDT) algorithm is identified to reconstructs the original dataset which can be viewed for future use even if the samples lost.
Agrawal R. and Srikant R. (May 2000), “Privacy Preserving Data Mining,” Proc. ACM SIGMOD Conf. Management of Data (SIGMOD ’00), pp. 439-450.
Dowd J., XuS., and Zhang. W (2006), “Privacy-Preserving Decision Tree Mining Based on Random Substitutions,” Proc. Int’l Conf. Emerging Trends in Information and Comm. Security (ETRICS ’06), pp. 145-159.
Goodin .D (Sept.2007), “Hackers Infiltrate TD Ameritrade client Database,” Retrieved September 2008, http://www.channelregister.co.uk/2007/09/15/ameritrade_database_burgled/.
Kaplan D. (May 2007), Hackers Steal 22,000 Social Security Numbers from Univ. of Missouri Database, Retrieved Sept. 2008, http://www.scmagazineus. com/ Hackers-steal-22000-Social Security numbers from University of Missouri database/article/34964/.
Liu L., Kantarcioglu M., and Thuraisingham B. (2009), “Privacy Preserving Decision Tree Mining from Perturbed Data,” Proc. 42nd Hawaii Int’l Conf. System Sciences (HICSS’09).
MaQ and Deng P. (2008), “Secure Multi-Party Protocols for Privacy Preserving Data Mining,” Proc. Third Int’l Conf. Wireless Algorithms, Systems, and Applications (WASA ’08), pp. 526-537.
Pui K. Fong and Jens H. Weber-Jahnke (February 2012), “Privacy Preserving Decision Tree Learning Using Unrealized Data Sets”, IEEE Transactions on Knowledge and Data Engineering, VOL.24, NO.2.
Sweeney L. (May 2002), “k-Anonymity: A Model for Protecting Privacy,” Int’l J. Uncertainty, Fuzziness and Knowledge-based Systems, vol. 10, pp. 557-570.
Vaidya J. And Clifton C. (July 2002), “Privacy Preserving Association Rule Mining in Vertically Partitioned Data,” Proc Eighth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’02), pp. 23-26.
Wang S.L. and Jafari A. (2005), “Hiding Sensitive Predictive Association Rules,” Proc. IEEE Int’l Conf. Systems, Man and Cybernetics, pp. 164- 169
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.