Abstract
Publication of the private set-valued data will provide enormous opportunities for counting queries and various data mining tasks. Compared to those previous methods based on partition-based privacy models (e.g., k-anonymity), differential privacy provides strong privacy guarantees against adversaries with arbitrary background knowledge. However, the existing solutions based on differential privacy for data publication are currently limited to static datasets, and do not adequately address today’s demand for up-to-date information. In this paper, we address the problem of differentially private set-valued data release on an incremental scenario in which the data need to be transformed are not static. Motivated by this, we propose an efficient algorithm, called IncTDPart, to incrementally generate a series of differentially private releases. The proposed algorithm is based on top-down partitioning model with the help of item-free taxonomy tree and update-bounded mechanism. Extensive experiments on real datasets confirm that our approach maintains high utility and scalability for counting query.
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References
Terrovitis, M., Mamoulis, N., Kalnis, P.: Privacy-preserving anonymization of set-valued data. In: Proceedings of the VLDB Endowment (PVLDB 2008), vol. 1(1), pp. 115–125 (2008)
He, Y., Naughton, J.F.: Anonymization of Set-Valued Data via Top-Down, Local Generalization. In: Proceedings of the VLDB Endowment (PVLDB 2009), vol. 2(1), pp. 934–945 (2009)
Ganta, S.R., Kasiviswanathan, S.P., Smith, A.: Composition attacks and auxiliary information in data privacy. In: Proc. of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 265–273 (2008)
Wong, R.C.W., Fu, A., Wang, K., Yu, P.S., Pei, J.: Can the utility of anonymized data be used for privacy breaches. ACM Transaction on Knowledge Discovery from Data (TKDD 2011) 5(3), 16 (2011)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006, Part II. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)
Kifer, D., Machanavajjhala, A.: No free lunch in data privacy. In: Proc. of the 31th International Conference on Management of Data (SIGMOD 2011), pp. 193–204 (2011)
Chen, R., Mohammed, N., Fung, B.C.M., Desai, B.C., Xiong, L.: Publishing Set-Valued Data via Differential Privacy. In: PVLDB 2011, vol. 4(11), pp. 1087–1098 (2011)
Chen, R., Fung, B.C.M., Desai, B.C., Sossou, N.M.: Differentially private transit data publication: a case study on the montreal transportation system. In: Proc. of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), pp. 213–221 (2012)
Dwork, C., Naor, M., Pitassi, T., Rothblum, G.N.: Differential pirvacy under continual observation. In: Proc. of the 42nd ACM Symposium on Theory of Computing (STOC 2010), pp. 715–724 (2010)
Chan, T.-H.H., Shi, E., Song, D.: Private and continual release of statistics. ACM Transactions on Information and System Security (TISS 2011) 14(3), 26 (2011)
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)
Xu, J., Zhang, Z., Xiao, X., Yang, Y., Yu, G.: Differentially private histogram publication. In: Proc. of the 28th International Conference on Data Engineering (ICDE 2012), pp. 32–43 (2012)
Hay, M., Rastogi, V., Miklau, G., Suciu, D.: Boosting the accuracy of differentially private histogram through consistency. In: Proceedings of the VLDB Endowment (PVLDB 2010), vol. 3(1), pp. 1021–1032 (2010)
Götz, M., Machanavajjhala, A., Wang, G., Xiao, X., Gehrke, J.: Publishing search logs - a comparative study of privacy guarantees. IEEE Transaction Knowledge and Data Engineering (TKDE 2012) 24(3), 520–532 (2012)
McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the netflix prize contenders. In: Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 627–636 (2009)
Byun, J.-W., Sohn, Y., Bertino, E., Li, N.: Secure anonymization for incremental datasets. In: Jonker, W., Petković, M. (eds.) SDM 2006. LNCS, vol. 4165, pp. 48–63. Springer, Heidelberg (2006)
Xiao, X., Tao, Y.: m-invariance: Towards privacy preserving republication of dynamic datasets. In: Proc. of the 27th International Conference on Management of Data (SIGMOD 2007), pp. 689–700 (2007)
McSherry, F.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proc. of the 29th International Conference on Management of Data (SIGMOD 2009), pp. 19–30 (2009)
Xiao, X., Bender, G., Hay, M., Gehrke, J.: iReduct: differential privacy with reduced relative errors. In: Proc. of the 31th International Conference on Management of Data (SIGMOD 2011), pp. 229–240 (2011)
Gormode, G., Procopiuc, M., Srivastava, D.: Differentially private summaries for sparse data. In: Proc. of the 15th International Conference on Database Theory (ICDT 2012), pp. 299–311 (2012)
UCI machine learning repository, http://archive.ics.uci.edu/ml
Frequent itemset mining dataset repository, http://fimi.ua.ac.be/data/
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Zhang, X., Meng, X., Chen, R. (2013). Differentially Private Set-Valued Data Release against Incremental Updates. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37487-6_30
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DOI: https://doi.org/10.1007/978-3-642-37487-6_30
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