Forecasting Using Rules Extracted from Privacy Preservation Neural Network

  • Nekuri Naveen
  • Vadlamani Ravi
  • Chillarige Raghavendra Rao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)


Privacy preserving data mining is of paramount importance in many areas. In this paper, we employ Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN) for preservation privacy in input feature values. The privacy preserved input features are fed to the Dynamic Evolving Neuro Fuzzy Inference System (DENFIS) and Classification and Regression Tree (CART) separately for rule extraction purpose. We also propose a new feature selection method using PSOAANN. Thus, in this study, PSOAANN accomplishes privacy preservation as well as feature selection. The performance of the hybrid is tested using 10 fold cross validation on 5 regression datasets viz. Auto MPG, Body Fat, Boston Housing, Forest Fires and Pollution. The study demonstrates the effectiveness of the proposed approach in generating accurate regression rules with and without feature selection. The ttest at 1% level of significance is performed to see whether the difference in results obtained in the case of with and without feature selection is statistically significant or not. In the case of PSOAANN + CART, it is observed that the result is statistical insignificant between with and without feature selection in four datasets. In the case of PSOAANN + DENFIS, it is observed that statistical significance between with and without feature selection for three datasets. Hence, from the t-test it is concluded that the proposed feature selection method yielded better or comparable results.


Privacy Preservation Privacy Preserved Auto Associative Neural Network Dynamic Evolving Fuzzy Inference System CART Feature Selection Rule Extraction Regression 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Preserving Privacy in Data Mining. In: ACM SIGMOD International Conference on Management of Data (May 2000)Google Scholar
  2. 2.
    Lindell, Y., Pinkas, B.: Privacy Preserving in Data Mining. In: Proceeding of the 20th Annual Cryptology Conference in Advances on Cryptology, pp. 36–54 (2000)Google Scholar
  3. 3.
    Xiao-Dan, W.U., Dian-Min, Y.U.E., Feng-Li, L.I.U., Yun-Feng, W., Chao-Hsien, C.H.: Privacy Preserving Data Mining Algorithms by Data Distortion. Management Science and Engineering, 223–228 (2006)Google Scholar
  4. 4.
    Behlen, F.M., Johnson, S.B.: Multicenter Patient Records Research: Security Policies and Tools. J. Am. Med. Inform. Assoc. 6(6), 435–443 (1999)CrossRefGoogle Scholar
  5. 5.
    Berman, J.J.: Confidentiality Issues for Medical Data Miners. Artificial Intelligent Med. 26(1-2), 25–36 (2002)CrossRefGoogle Scholar
  6. 6.
    Thuraisingham, B.: Web Data Mining and its Applications in Business Intelligence and Counter-terrorism. CRC Press (2003)Google Scholar
  7. 7.
    Fienberg, S.E.: Homeland insecurity: Data mining, terrorism detection, and confidentiality. In: Australian Bureau of Statistics, 55th Session of the International Statistical Institute (ISI), Sydney (2005)Google Scholar
  8. 8.
    Sweeney, L.: Privacy-Preserving Bio-terrorism Surveillance. In: AAAI Spring Symposium, AI Technologies for Homeland Security (2005)Google Scholar
  9. 9.
    Oliveira, S.R.M., Zaiane, O.R.: A privacy-preserving clustering approach toward secure and effective data analysis for business collaboration. Journal of Computer and Security 26, 81–83 (2007)CrossRefGoogle Scholar
  10. 10.
    Boyens, C., Krishnan, R., Padman, R.: On privacy-preserving access to distributed heterogeneous healthcare information. In: Proceedings of the 37th International Conference on Annual Hawaii System Sciences (2004)Google Scholar
  11. 11.
    Bertino, E.: A Framework for Evaluating Privacy Preserving Data Mining Algorithms. Data Mining and Knowledge Discovery 11, 121–154 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Granmo, O.C., Oleshchuk, V.A.: Privacy Preserving Data Mining in Telecommunication Services. International Journal of Computing 3(4), 85–90 (2005)Google Scholar
  13. 13.
    Vaidya, J., Clifton, C., Zhu, M.: Privacy Preserving Data Mining. In: Advances in Information Security, vol. 19. Springer, Heidelberg (2006) ISBN: 978-0-387-25886-7Google Scholar
  14. 14.
    Crises, G.: Non-Perturbative Methods for Microdata Privacy in Statistical Databases (2004),
  15. 15.
    Pinkas, B.: Cryptographic techniques for privacy-preserving data mining. SIGKDD Explorations 4 (2002)Google Scholar
  16. 16.
    Ramu, K., Ravi, V.: Privacy preservation in data mining using hybrid perturbation methods: an application to bankruptcy prediction in banks. International Journal Data Analysis Techniques and Strategies 1(4), 313–331 (2009)CrossRefGoogle Scholar
  17. 17.
    Bansal, A., Chen, T., Zhong, S.: Privacy Preserving Back-Propagation neural network learning over arbitrarily partitioned data. Journal of Neuro Computing and Applications, 1433–3058 (2010)Google Scholar
  18. 18.
    Paramjeet, Ravi, V., Naveen, N., Raghavendra Rao, C.: Privacy Preserving Data Mining using Particle Swarm Optimization trained Auto-Associative Neural Network: an Application to Bankruptcy Prediction in Banks (Accepted International Journal of Data Mining Modeling and Management)Google Scholar
  19. 19.
    Naveen, N., Ravi, V., Raghavendra Rao, C.: Rule Extraction from Privacy Preserving Neural Network: Application to Banking. In: International Conference on Control, Robotics and Cybernetics (ICCRC 2011), India, March 21-24, pp. 408–412 (2011)Google Scholar
  20. 20.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of IEEE International Conference on Neural Networks, Piscataway, NJ, USA, pp. 1942–1948 (1995)Google Scholar
  21. 21.
    Kasabov, N., Song, Q.: DENFIS: Dynamic, evolving neural-fuzzy inference systems and its application for time-series prediction. IEEE Transactions on Fuzzy Systems 10, 144–154 (2002)CrossRefGoogle Scholar
  22. 22.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International Group, Belmont, California (1984)zbMATHGoogle Scholar
  23. 23.
    Hruschka, H., Natter, M.: Comparing performance of feedforward neural nets and K-means for cluster-based market segmentation. European Journal of Operational Research 114, 346–353 (1999)CrossRefzbMATHGoogle Scholar
  24. 24.
    Kramer, M.A.: Nonlinear principal component analysis using auto associative neural networks. AIChE Journal 37(2), 233–243 (1991)CrossRefGoogle Scholar
  25. 25.
    Ravi, V., Pramodh, C.: Non-linear principal component analysis-based hybrid classifiers: an application to bankruptcy prediction in banks. International Journal of Information and Decision Sciences 2(1), 50–67 (2010)CrossRefGoogle Scholar
  26. 26.
    Guyon, B., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  27. 27.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007)Google Scholar
  28. 28.
    Penrose, K.W., Nelson, A.G., Fisher, A.G.: FACSM, Human Performance Research Center, Brigham Young University. Provo, Utah 84602 as listed in Medicine and Science in Sports and Exercise 17(2), 189 (1985)CrossRefGoogle Scholar
  29. 29.
    Cortez, P., Morais, A.: A Data Mining Approach to Predict Forest Fires using Meteorological Data. In: Neves, J., Santos, M.F., Machado, J. (eds.) New Trends in Artificial Intelligence, Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, Guimarães, Portugal, pp. 512–523 (2007)Google Scholar
  30. 30.
    McDonald, G.C., Schwing, R.C.: Instabilities of regression estimates relating air Pollution to mortality. Technometrics 15, 463–482 (1973)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nekuri Naveen
    • 1
    • 2
  • Vadlamani Ravi
    • 1
  • Chillarige Raghavendra Rao
    • 2
  1. 1.Institute for Development and Research in banking TechnologyHyderabadIndia
  2. 2.Department of Computer & Information SciencesUniversity of HyderabadHyderabadIndia

Personalised recommendations