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Rank Swapping for Stream Data

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Book cover Modeling Decisions for Artificial Intelligence (MDAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8825))

Abstract

We propose the application of rank swapping to anonymize data streams. We study the viability of our proposal in terms of information loss, showing some promising results. Our proposal, although preliminary, provides a simple and parallelizable solution to anonymize data stream.

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References

  1. Aggarwal, C.C. (ed.): Managing and Mining Sensor Data. Springer (2013)

    Google Scholar 

  2. Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013), http://archive.ics.uci.edu/ml

    Google Scholar 

  3. Byun, J.-W., Li, T., Bertino, E., Li, N., Sohn, Y.: Privacy-preserving incremental data dissemination. Journal of Computer Security 17(1), 43–68 (2009)

    Google Scholar 

  4. Cao, J., Carminati, B., Ferrari, E., Tan, K.-L.: CASTLE: Continuously Anonymizing Data Streams. IEEE Transactions on Dependable and Secure Computing 8(3), 337–352 (2011)

    Article  Google Scholar 

  5. Domingo-Ferrer, J., Torra, V.: Disclosure Control Methods and Information Loss for Microdata. In: Doyle, P., Lane, J.I., Theeuwes, J.J.M., Zayatz, L. (eds.) Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, pp. 91–110. Elsevier Science (2001)

    Google Scholar 

  6. Domingo-Ferrer, J., Torra, V.: A quantitative comparison of disclosure control methods for microdata. In: Doyle, P., Lane, J.I., Theeuwes, J.J.M., Zayatz, L. (eds.) Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, pp. 111–134. North-Holland (2001)

    Google Scholar 

  7. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining Data Streams: A Review. SIGMOD Rec. 34(2), 18–26 (2005)

    Article  Google Scholar 

  8. Ghinita, G., Karras, P., Kalnis, P., Mamoulis, N.: Fast Data Anonymization with Low Information Loss. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 758–769. VLDB Endowment, Vienna (2007)

    Google Scholar 

  9. Li, J., Ooi, B.C., Wang, W.: Anonymizing Streaming Data for Privacy Protection. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 1367–1369 (2008)

    Google Scholar 

  10. Martinez-Bea, S., Torra, V.: Trajectory anonymization from a time series perspective. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 401–408 (2011)

    Google Scholar 

  11. Mateo-Sanz, J.M., Sebé, F., Domingo-Ferrer, J.: Outlier Protection in Continuous Microdata Masking. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 201–215. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Moore, R.: Controlled data swapping techniques for masking public use microdata sets, U. S. Bureau of the Census (unpublished manuscript) (1996)

    Google Scholar 

  13. Navarro-Arribas, G., Torra, V.: Privacy-preserving data-mining through micro-aggregation for web-based e-commerce. Internet Research 20, 366–384 (2010)

    Article  Google Scholar 

  14. Nin, J., Torra, V.: Towards the evaluation of time series protection methods. Inf. Sci. 179(11), 1663–1677 (2009)

    Article  MATH  Google Scholar 

  15. ONS, Statistical disclosure control (sdc) methods short-listed for 2011 UK census tabular outputs, SDC UKCDMAC Subgroup Paper 1, Office for National Statistics, UK (2011)

    Google Scholar 

  16. Pei, J., Xu, J., Wang, Z., Wang, W., Wang, K.: Maintaining K-Anonymity against Incremental Updates. In: 19th International Conference on Scientific and Statistical Database Management (2007)

    Google Scholar 

  17. Reiss, S.: Practical data-swapping: The first steps. In: IEEE Symposium on Security and Privacy, pp. 38–43 (1980)

    Google Scholar 

  18. Samarati, P.: Protecting respondents identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13, 1010–1027 (2001)

    Article  Google Scholar 

  19. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10, 557–570 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  20. Templ, M.: Statistical Disclosure Control for Microdata Using the R-Package sdcMicro. Transactions on Data Privacy 1(2), 67–85 (2008)

    MathSciNet  Google Scholar 

  21. Torra, V.: Rank Swapping for Partial Orders and Continuous Variables. Presented at the International Conference on Availability, Reliability and Security, ARES 2009, pp. 888–893 (2009)

    Google Scholar 

  22. Truta, T.M., Campan, A.: K-anonymization incremental maintenance and optimization techniques. In: Proceedings of the 2007 ACM Symposium on Applied Computing, pp. 380–387. ACM Press (2007)

    Google Scholar 

  23. Wu, X., Zhu, X., Wu, G.-Q., Ding, W.: Data mining with big data. IEEE Transactions on Knowledge and Data Engineering 26(1), 97–107 (2014)

    Article  Google Scholar 

  24. Xiao, X., Tao, Y.: M-invariance: towards privacy preserving re-publication of dynamic datasets. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD 2007, pp. 689–700. ACM (2007)

    Google Scholar 

  25. Yancey, W.E., Winkler, W.E., Creecy, R.H.: Disclosure Risk Assessment in Perturbative Microdata Protection. In: Domingo-Ferrer, J. (ed.) Inference Control in Statistical Databases. LNCS, vol. 2316, pp. 135–152. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  26. Zakerzadeh, H., Osborn, S.L.: FAANST: Fast Anonymizing Algorithm for Numerical Streaming DaTa. In: Garcia-Alfaro, J., Navarro-Arribas, G., Cavalli, A., Leneutre, J. (eds.) DPM 2010 and SETOP 2010. LNCS, vol. 6514, pp. 36–50. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  27. Zhou, B., Han, Y., Pei, J., Jiang, B., Tao, Y., Jia, Y.: Continuous privacy preserving publishing of data streams. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT 2009, pp. 648–659. ACM (2009)

    Google Scholar 

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Navarro-Arribas, G., Torra, V. (2014). Rank Swapping for Stream Data. In: Torra, V., Narukawa, Y., Endo, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2014. Lecture Notes in Computer Science(), vol 8825. Springer, Cham. https://doi.org/10.1007/978-3-319-12054-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-12054-6_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12053-9

  • Online ISBN: 978-3-319-12054-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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