Abstract
The privacy preserving data mining is a research hotspot. Most of the privacy preserving algorithms are focused on the centralized database. The algorithms on the distributed database are very vulnerable to collusion attack. The Privacy-Preserving data mining algorithm based on particle swarm optimization is proposed in this paper. The algorithm is based on centralized database, and it can be used on the distributed database. The algorithm is divided into two steps in the distributed database. In the first step, the modified particle swarm optimization algorithm is used to get the local Bayesian network structure. The purpose of the second step is getting the global Bayesian network structure by using local ones. In order to protect the data privacy, the secure sum is used in the algorithm. The algorithm is proved to be convergent on theory. Some experiments have been done on the algorithm, and the results prove that the algorithm is feasible.
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
Xu, L.J., Huang, J.G., Wang, H.J., Long, B.: Hybrid Optimized Algorithm for Learning Bayesian Network Structure. Journal of Computer-Aided Design & Computer Graphics 21(5), 633–639 (2009)
Zhao, J.S., Li, Y.G., Zhang, Y.P.: An Improved Quantum Ant Colony Algorithm and Its Application. Computer Application and Software 27(7), 133–138 (2010)
Xing, C.H., Zhang, Q., Xian, H.W.: Research on Learning Bayesian Networks by Particle Swarm Optimization. Information Technology Journal 5(3), 540–545 (2006)
Li, M.W., Kang, K.G., Zhou, P.F., Hong, W.C.: Hybrid Optimization Algorithm Based on Chaos, Cloud and Particle Swarm Optimization Algorithm. Journal of Systems Engineering and Electronics 24(2), 324–334 (2013)
Wang, H.M.: Research on Privacy-Preserving Bayesian Network Learning, Dissertation for Philosophy Degree in Tianjin University (2006)
Wang, H.S., Liu, G., Qi, Z.H.: BIC Scoring Bayesian Network Model and Its Application. Computer Engineering 34(15), 229–230 (2008)
Sheng, Y., Pan, H., Xia, L., et al.: Hybrid Chaos Particle Swarm Optimization Algorithm and Application in Benzene-toluene Flash Vaporization. Journal of Zhejiang University of Technology 38(3), 319–322 (2010)
Dong, Y., Guo, H.: Adaptive chaos particle swarm optimization based on colony fitness variance. Application Research of Computers 28(3), 855–859 (2011)
Haishu, W., Gang, L., Zhaohui, Q.: BIC Scoring Bayesian Network Model and Its Application. Computer Engineering 34(15), 229–230 (2008)
Yuyuan, K., Jintao, Y., Qiang, L., Shenglin, Z., Minghu, Z.: Bayesian Network Classifier based on PSO with predatory escape behavior. Journal of Computer Application 31(2), 454–457 (2011)
Lu, X., Huang, K., Lian, G.: Optimizing strategy of system level fault diagnosis based on chaos particle swarm optimization. Systems Engineering and Electronics 32(1), 217–220 (2010)
Yu, S., Wei, Y., Zhu, K.: Hybrid optimization algorithms based on particle swarm optimization and genetic algorithm. Systems Engineering and Electronics 33(7), 1648–1652 (2011)
Zhang, Y., Shao, S.: Cloud hyper mutation particle swarm optimization algorithm based on cloud model. Pattern Recognition and Artificial Intelligence 24(1), 91–95 (2010)
Liu, H., Zhou, Y.: A cloud adaptive particle swarm optimization algorithm based on mean. Computer Engineering & Science 33(5), 97–100 (2011)
Hongmei, W., Yuan, Z., Zheng, Z., Chengshan, W.: Privacy-preserving Bayesian network Learning on distributed heterogeneous data. Journal of Tianjin University 40(9) (2007)
Weiping, G., Wei, W., Haofeng, Z., Baile, S.: Privacy Preserving classification Mining. Journal of Computer Research and Development 43(1), 39–45 (2006)
Wang, J.: Matrix Decomposition for Data Disclosure Control and Data Mining Applications, Dissertation for Philosophy Degree in University of Kentucky (2008)
Han, X., Min, J., Ma, H.-X.: Soving Fuzzy Chance-chonstrained Programming Using Chaos Quantum ACO Algorithm and Its Convergence. Journal of System Simulation 21(20), 6462–6468 (2009)
Huang, J.-W., Qin, C.-Y.: Real-coded quantum ant colony optimization algorithm for global numerical optimization. Application Research of Computer 26(10), 3660–3662 (2009)
Ashrafi, M.Z., Taniar, D., Smith, K.: Towards Privacy Preserving Distributed Association Rule Mining. In: Das, S.R., Das, S.K. (eds.) IWDC 2003. LNCS, vol. 2918, pp. 279–289. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yang, L., Wu, J., Peng, L., Liu, F. (2014). Privacy-Preserving Data Mining Algorithm Based on Modified Particle Swarm Optimization. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-09339-0_54
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09338-3
Online ISBN: 978-3-319-09339-0
eBook Packages: Computer ScienceComputer Science (R0)