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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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)
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)
Sweeney, L.: K-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
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)
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)
Vaidya, J., Yu, H., Jiang, X.: Privacy Preserving SVM Classification. Knowledge and Information Systems 14, 161–178 (2007)
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)
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)
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)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
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)
Berger, M., Gostiaux, B.: Differential Geometry: Manifolds, Curves and Surfaces, GTM115. Springer, Heidelberg (1974)
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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
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