Abstract
Protecting the private graph data while releasing accurate estimate of the data is one of the most challenging problems in data privacy. Node strength combines the topological information with the weight distribution of the weighted graph in a natural way. Since an edge in graph data oftentimes represents relationship between two nodes, edge-differential privacy (edge-DP) can protect relationship between two entities from being disclosed. In this paper, we investigate the problem of publishing the node strength histogram of a private graph under edge-DP. We propose two clustering approaches based on sequence-aware and local density to aggregate histogram. Our experimental study demonstrates that our approaches can greatly reduce the error of approximating the true node strength histogram.
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References
Yin, H., Cui, B., Zhou, X., Wang, W., Huang, Z., Sadiq, S.W.: Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Trans. Inf. Syst. 35(2), 11:1–11:44 (2016)
Yin, H., Chen, H., Sun, X., Wang, H., Wang, Y., Nguyen, Q.V.H.: SPTF: a scalable probabilistic tensor factorization model for semantic-aware behavior prediction. In: ICDM, pp. 585–594 (2017)
Yin, H., Cui, B.: Spatio-Temporal Recommendation in Social Media. Springer Briefs in Computer Science. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-0748-4
Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Hung, N.Q.V.: Adapting to user interest drift for POI recommendation. IEEE Trans. Knowl. Data Eng. 28(10), 2566–2581 (2016)
Zhao, Y., Zhang, Z., Wang, Y., Liu, J.: Robust mobile spamming detection via graph patterns. In: ICPR, pp. 983–986 (2012)
Thedchanamoorthy, G., Piraveenan, M., Uddin, S., Senanayake, U.: Influence of vaccination strategies and topology on the herd immunity of complex networks. Soc. Netw. Anal. Min. 4(1), 213 (2014)
Backstrom, L., Dwork, C., Kleinberg, J.M.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: WWW, pp. 181–190 (2007)
Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: Symposium on Security and Privacy, pp. 173–187 (2009)
Proserpio, D., Goldberg, S., McSherry, F.: Calibrating data to sensitivity in private data analysis. PVLDB 7, 637–648 (2014)
Zhang, J., Cormode, G., Procopiuc, C.M., Srivastava, D., Xiao, X.: Private release of graph statistics using ladder functions. In: SIGMOD, pp. 731–745 (2015)
Zhu, T., Li, G., Zhou, W., Yu, P.S.: Differentially private data publishing and analysis: a survey. IEEE Trans. Knowl. Data Eng. 29, 1619–1638 (2017)
Hay, M., Li, C., Miklau, G., Jensen, D.D.: Accurate estimation of the degree distribution of private networks. In: ICDM, pp. 169–178 (2009)
Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88–90 (2006)
Day, W., Li, N., Lyu, M.: Publishing graph degree distribution with node differential privacy. In: SIGMOD, pp. 123–138 (2016)
Kasiviswanathan, S.P., Nissim, K., Raskhodnikova, S., Smith, A.: Analyzing graphs with node differential privacy. In: Sahai, A. (ed.) TCC 2013. LNCS, vol. 7785, pp. 457–476. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36594-2_26
Stanford large network dataset collection. http://snap.stanford.edu/data/
Dwork, C.: Differential privacy. In: ICALP, pp. 1–12 (2006)
McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: FOCS, pp. 94–103 (2007)
McSherry, F.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: SIGMOD, pp. 19–30 (2009)
Kifer, D., Lin, B.: Towards an axiomatization of statistical privacy and utility. In: SIGMOD, pp. 147–158 (2010)
Xu, J., Zhang, Z., Xiao, X., Yang, Y., Yu, G., Winslett, M.: Differentially private histogram publication. VLDB 22(6), 797–822 (2013)
Li, C., Hay, M., Miklau, G., Wang, Y.: A data- and workload-aware query answering algorithm for range queries under differential privacy. PVLDB 7(5), 341–352 (2014)
Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)
Raskhodnikova, S., Smith, A.D.: Efficient lipschitz extensions for high-dimensional graph statistics and node private degree distributions. CoRR, abs/1504.07912 (2015)
Nissim, K., Raskhodnikova, S., Smith, A.D.: Smooth sensitivity and sampling in private data analysis. In: STOC, pp. 75–84 (2007)
Sala, A., Zhao, X., Wilson, C., Zheng, H., Zhao, B.Y.: Sharing graphs using differentially private graph models. In: SIGCOMM, pp. 81–98 (2011)
Zhang, X., Chen, R., Xu, J., Meng, X., Xie, Y.: Towards accurate histogram publication under differential privacy. In: ICDM, pp. 587–595 (2014)
Qardaji, W.H., Yang, W., Li, N.: Understanding hierarchical methods for differentially private histograms. PVLDB 6(14), 1954–1965 (2013)
Ács, G., Castelluccia, C., Chen, R.: Differentially private histogram publishing through lossy compression. In: ICDM, pp. 1–10 (2012)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61572335, 61572336, 61472263, 61402312 and 61402313, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223, and Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.
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Qian, Q., Li, Z., Zhao, P., Chen, W., Yin, H., Zhao, L. (2018). Publishing Graph Node Strength Histogram with Edge Differential Privacy. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_5
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DOI: https://doi.org/10.1007/978-3-319-91458-9_5
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