Incorporating Privacy Concerns in Data Mining on Distributed Data

  • Hui-zhang Shen
  • Ji-di Zhao
  • Ruipu Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4183)


Data mining, with its objective to efficiently discover valuable and inherent information from large databases, is particularly sensitive to misuse. Therefore an interesting new direction for data mining research is the development of techniques that incorporate privacy concerns and to develop accurate models without access to precise information in individual data records. The difficulty lies in the fact that the two metrics for evaluating privacy preserving data mining methods: privacy and accuracy are typically contradictory in nature. We address privacy preserving mining on distributed data in this paper and present an algorithm, based on the combination of probabilistic approach and cryptographic approach, to protect high privacy of individual information and at the same time acquire a high level of accuracy in the mining result.


Association Rule Minimum Support Frequent Itemsets Markov Chain Model Transition Probability Matrix 
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  1. 1.
    Clifton, C., Marks, D.: Security and privacy implications of data mining. In: ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (May 1996)Google Scholar
  2. 2.
    Estivill-Castro, V., Brankovic, L.: Data swapping: Balancing privacy against precision in mining for logic rules. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 389–398. Springer, Heidelberg (1999)Google Scholar
  3. 3.
    Agrawal, R.: Data Mining: Crossing the Chasm. In: The 5th International Conference on Knowledge Discovery in Databases and Data Mining, San Diego, California (August 1999) (an invited talk at SIGKDD)Google Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Privacy preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, Dallas, Texas (May 2000)Google Scholar
  5. 5.
    Agrawal, D., Aggarwal, C.: On the Design and Quantification of Privacy Preserving Data Mining Algorithms. In: Proc. of 20th ACM Symp. on Principles of Database Systems (PODS) (2001)Google Scholar
  6. 6.
    Conway, R., Strip, D.: Selective partial access to a database. In: Proc. ACM Annual Conf. (1976)Google Scholar
  7. 7.
    Breiman, L., et al.: Classification and Regression Trees. Wadsworth, Belmont (1984)MATHGoogle Scholar
  8. 8.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)Google Scholar
  9. 9.
    Evfimievski, A., et al.: Privacy Preserving Mining of Association Rules. Information Systems 29(4), 343–364 (2004)CrossRefGoogle Scholar
  10. 10.
    Rizvi, S.J., Haritsa, J.R.: Maintaining Data Privacy in Association Rule Mining. In: Proc. 28th International Conf. Very Large Data Bases (2002)Google Scholar
  11. 11.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: IBM Almaden Research Center, San Jose, California (June 1994)Google Scholar
  12. 12.
    Adam, R., Wortman, J.C.: Security-control methods for statistical databases. ACM Computing Surveys 21(4), 515–556 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hui-zhang Shen
    • 1
  • Ji-di Zhao
    • 1
  • Ruipu Yao
    • 2
  1. 1.Aetna School of ManagementShanghai Jiao Tong UniversityShanghaiP.R. China
  2. 2.School of Information EngineeringTianjin University of CommerceTianjinP.R. China

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