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Nearest Neighbor Tour Circuit Encryption Algorithm Based Random Isomap Reduction

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Advanced Data Mining and Applications (ADMA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5678))

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Abstract

This paper presents nearest neighbor tour circuit encryption algorithm based random Isomap reduction. In order to be suited for privacy-preserving classification, we first alter the selection fashion of the parameters nearest neighbor number k and embedded space dimension d of Isomap reduction algorithm. Further we embed the tourists’ sensitive attribution into random dimension space using random Isomap reduction, thus the sensitive attributes are encrypted and protected. Because the transformed space dimension d and nearest neighbor number k are both random, this algorithm is not easily be breached. In addition, Isomap can keep geodesic distance of two points of dataset, so the precision change of classification after encryption can be controlled in a small scope . The experiment show that if we select appropriate parameters, then nearest neighbors of every point may be completely consistent. The present algorithm can guarantee that the security and the precision both achieve the requirements.

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References

  1. Agrawal, R., Srikant, R.: Privacy-Preserving Data mining. In: 2000 ACM SIGMOD International Conference on Management of Data, pp. 439–450. ACM Press, Dallas (2000)

    Chapter  Google Scholar 

  2. Agrawal, S., Haritsa, J.R.: A Framework for High-Accuracy Privacy-Preserving Mining. In: 2005 IEEE International Conference on Data Engineer (ICDE), pp. 193–204. IEEE Press, Tokyo (2005)

    Google Scholar 

  3. Sweeney, L.: K-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Xu, S., Zhang, J., Han, D., Wang, J.: Singular Value Decomposition Based Data Distortion Strategy for Privacy Distortion. Knowledge and Information System 10(3), 383–397 (2006)

    Article  Google Scholar 

  5. Vaidya, J., Clifton, C.: Privacy Preserving K-means Clustering over Vertically Portioned Data. In: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 206–215. ACM Press, Washington (2003)

    Google Scholar 

  6. Vaidya, J., Yu, H., Jiang, X.: Privacy Preserving SVM Classification. Knowledge and Information Systems 14, 161–178 (2007)

    Article  Google Scholar 

  7. Chen, K., Liu, L.: A Random Rotation Perturbation Approach to Privacy Data Classification. In: 2005 IEEE International Conference on Data Mining (ICDM), pp. 589–592. IEEE Press, Houston (2005)

    Google Scholar 

  8. Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: Random Data Perturbation Techniques and Privacy Preserving Data Mining. Knowledge and Information Systems 7, 387–414 (2005)

    Article  Google Scholar 

  9. Huang, Z., Du, W., Chen, B.: Deriving Private Information from Randomized Data. In: 2005 ACM SIGMOD International Conference on Management of Data, pp. 37–48. ACM Press, Baltimore (2005)

    Chapter  Google Scholar 

  10. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)

    Article  Google Scholar 

  11. Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  12. de Silva, V., Tenenbaum, J.B.: Global Versus Local Methods in Nonlinear Dimensionality Reduction. In: Advances in Neural Information Processing Systems 15 (NIPS 2002), pp. 705–712. MIT Press, Cambridge (2003)

    Google Scholar 

  13. Berger, M., Gostiaux, B.: Differential Geometry: Manifolds, Curves and Surfaces, GTM115. Springer, Heidelberg (1974)

    MATH  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Lu, W., Yao, Za. (2009). Nearest Neighbor Tour Circuit Encryption Algorithm Based Random Isomap Reduction. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_25

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  • DOI: https://doi.org/10.1007/978-3-642-03348-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03347-6

  • Online ISBN: 978-3-642-03348-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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