Abstract
In recent years, differential privacy data publishing has received considerable attention. However, existing techniques on achieving differential privacy for answering range-count queries fail to release data with high quality. In this paper, we propose a new solution for answering range-count queries under the framework of ε-differential privacy, which aims to maintain high data utility while protecting individual privacy. The key idea of the proposed solution is to add noise on an average tree, in which each node value is the average value of all its leaf nodes. Experimental analysis is designed by comparing the proposed solution and the classic algorithms on the released data utility. The theoretical analysis and experimental results show that our solution is effective and feasible.
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References
Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: A survey on recent developments. ACM Computing Surveys 42(4) (2010)
Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data 1(1), Article 3 (2007)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 106–115 (2007)
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating Noise to Sensitivity in Private Data Analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006)
Dwork, C.: Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)
Dwork, C.: Differential Privacy: A Survey of Results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008)
Dwork, C., Smith, A.: Differential privacy for statistics: what we know and what we want to learn. Journal of Privacy and Confidentiality 1(2), 135–154 (2009)
Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical privacy: the sulq framework. In: PODS, Baltimore, Maryland, pp. 128–138 (2005)
Xiao, X., Tao, Y.: Output perturbation with query relaxation. Journal Proceedings of the VLDB Endowment, 857–869 (2008)
Machanavajjhala, A., Kifer, D., Abowd, J.M., Gehrke, J., Vilhuber, L.: Privacy: Theory meets practice on the map. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 277–286 (2008)
Korolova, A., Kenthapadi, K., Mishra, N., Ntoulas, A.: Releasing search queries and clicks privately. In: WWW (2009)
Gotz, M., Machanavajjhala, A., Wang, G., Xiao, X., Gehrke, J.: Privacy in search logs. CoRR, abs/0904.0682 (2009)
Jagannathan, G., Pillaipakkamnatt, K., Wright, R.N.: A practical differentially private random decision tree classifier. In: IEEE International Conference on Data Mining Workshops, ICDMW 2009, pp. 114–121 (2009)
Friedman, A., Schuster, A.: Data mining with differential privacy. In: KDD 2010, Washington, DC, USA, pp. 493–502 (2010)
Xiao, X., Bender, G., Hay, M., Gehrke, J.: iReduct: differential privacy with reduced relative errors. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, New York, USA, pp. 229–240 (2011)
Xiao, X., Wang, G., Gehrke, J.: Differential privacy via wavelet transforms. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE), pp. 225–236 (2010)
Hay, M., Rastogi, V., Miklau, G., Suciu, D.: Boosting the accuracy of differentially private histograms through consistency. Proceedings of the VLDB Endowment 3(1), 1021–1032 (2010)
Amazon Access Samples Data Set, http://archive.ics.uci.edu/ml/index.html
American Community Survey 2006-2010 Acs 5-Year Pums, http://www.census.gov/
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Zhang, X., Wu, Y., Wang, X. (2012). Differential Privacy Data Release through Adding Noise on Average Value. In: Xu, L., Bertino, E., Mu, Y. (eds) Network and System Security. NSS 2012. Lecture Notes in Computer Science, vol 7645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34601-9_32
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DOI: https://doi.org/10.1007/978-3-642-34601-9_32
Publisher Name: Springer, Berlin, Heidelberg
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