Abstract
Privacy preserving data publishing is one of the most important issues of privacy preserving data mining, but the problem of privately publishing time series data has not received enough attention. Random perturbation is an efficient method of privately publishing data. Random noise addition introduces uncertainty into published data, increasing the difficult of conjecturing the original values. The existing Gaussian white noise addition distributes the same amount of noise to every single attribute of each series, incurring the great decrease of data utility for classification purpose. Through analyzing the different impact of local regions on overall classification pattern, we formally define the concept of discord region which strongly influences the classification performance. We perturb original series differentially according to their position, whether in a discord region, to improve classification utility of published data. The experimental results on real and synthetic data verify the effectiveness of our proposed methods.
This is a preview of subscription content, log in via an institution to check access.
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
Yabo, X., Ke, W., Ada, W.C.F., Rong, S., Jian, P.: Privacy-Preserving Data Stream Classification. In: Charu, A., Philip, S.Y. (eds.) Privacy-Preserving Data Mining Models and Algorithms, pp. 487–510. Springer, Heidelberg (2008)
Ye, Z., Yongjian, F., Huirong, F.: On Privacy in Time Series Data Mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 479–493. Springer, Heidelberg (2008)
Josenildo, C.S., Matthias, K.: Privacy-preserving discovery of frequent patterns in time series. In: Perner, P. (ed.) ICDM 2007. LNCS (LNAI), vol. 4597, pp. 318–328. Springer, Heidelberg (2007)
Nin, J., Torra, V.: Towards the evaluation of time series protection methods. Information Science 179, 1663–1677 (2009)
Feifei, L., Sun, J., Papadimitriou, S., Mihaila, G., Stanoi, I.: Hiding in the crowd: Privacy preservation on evolving streams through correlation tracking. In: 23rd International Conference on Data Engineering, pp. 686–695. IEEE, Los Alamitos (2007)
Papadimitriou, S., Feifei, L., Kollios, G., Philip, S.Y.: Time series compressibility and privacy. In: 33rd International Conference on Very Large Data Bases, pp. 459–470. ACM, New York (2007)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 571–588 (2002)
Yubao, L., Xiuwei, C., Fei, W., Jian, Y.: Efficient Detection of Discords for Time Series Stream. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, Q.-M. (eds.) APWeb/WAIM 2009. LNCS, vol. 5446, pp. 629–634. Springer, Heidelberg (2009)
Lindell, Y., Pinkas, B.: Privacy Preserving Data Mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)
Agrawal, R., Aggarwal, C.C.: Privacy preserving data mining. In: ACM SIGMOD International Conference on Management of Data, pp. 439–450. ACM, New York (2000)
Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.: Fast time series classification using numerosity reduction. In: International Conference on Machine Learning, pp. 1033–1040. ACM, New York (2006)
Kifer, D., Gehrke, J.: Injecting utility into anonymized datasets. In: ACM SIGMOD International Conference on Management of Data, pp. 217–228. ACM, New York (2006)
Evfimievski, A., Gehrke, J., Srikant, R.: Limiting privacy breaches in privacy preserving data mining. In: ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 211–222. ACM, New York (2003)
Abe, S., Lan, M.S.: A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Trans. Fuzzy Systems 3, 18–28 (1995)
Benjamin, C.M.F., Ke, W., Philip, S.Y.: Top-Down Specialization for Information and Privacy Preservation. In: 21st International Conference on Data Engineering, pp. 205–216. IEEE Computer Society, Los Alamitos (2005)
Ivan, D., Yuval, I.: Scalable Secure Multiparty Computation. In: Dwork, C. (ed.) CRYPTO 2006. LNCS, vol. 4117, pp. 501–520. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jin, S., Liu, Y., Li, Z. (2010). Discord Region Based Analysis to Improve Data Utility of Privately Published Time Series. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-17316-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17315-8
Online ISBN: 978-3-642-17316-5
eBook Packages: Computer ScienceComputer Science (R0)