Abstract
With the increasing popularity of online social networks, such as twitter and weibo, privacy preserving publishing of social network data has raised serious concerns. Previous works only consider a single static release of social network data, which are not inadequate for analyzing the evolution of social networks. In this paper, we focus on the problem of preserving edges when edges are deleted or added in multiple releases of social network data. To achieve this objective, we propose the Dynamic Safety Condition, which effectively constrains nodes partition to ensure sparsity of edges between any two group. Using this condition, we devise the heuristic algorithm DEP, which anonymizes a sequential graphs to satisfy the privacy objective. Finally, we verify the effectiveness of the algorithm through experiments.
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References
Bhagat, S., Cormode, G., Krishnamurthy, B., Srivastava, D.: Class-based graph anonymization for social network data. Proceedings of the VLDB Endowment 2(1), 766–777 (2009)
Bhagat, S., Cormode, G., Srivastava, D., Krishnamurthy, B.: Prediction promotes privacy in dynamic social networks. In: Proceedings of the 3rd Conference on Online Social Networks, p. 6. USENIX Association (2010)
Wang, C.-J.L., Wang, E.T., Chen, A.L.: Anonymization for multiple released social network graphs, pp. 99–110 (2013)
Yuan, M., Chen, L., Yu, P.S.: Personalized privacy protection in social networks. Proceedings of the VLDB Endowment 4(2), 141–150 (2010)
Fisman, R., Iyengar, S., Kamenica, E., Simonson, I.: Gender differences in mate selection: Evidence from a speed dating experiment. The Quarterly Journal of Economics 121(2), 673–697 (2006)
Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: Proceedings of the 16th International Conference on World Wide Web, pp. 181–190. ACM (2007)
Hay, M., Miklau, G., Jensen, D., Towsley, D., Weis, P.: Resisting structural re-identification in anonymized social networks. Proceedings of the VLDB Endowment 1(1), 102–114 (2008)
Zheleva, E., Getoor, L.: Preserving the privacy of sensitive relationships in graph data. In: Bonchi, F., Malin, B., Saygın, Y. (eds.) PInKDD 2007. LNCS, vol. 4890, pp. 153–171. Springer, Heidelberg (2008)
Campan, A., Truta, T.M.: A clustering approach for data and structural anonymity in social networks. In: Privacy, Security, and Trust in KDD Workshop, PinKDD 2008 (2008)
Cormode, G., Srivastava, D., Yu, T., Zhang, Q.: Anonymizing bipartite graph data using safe groupings. Proceedings of the VLDB Endowment 1(1), 833–844 (2008)
Tai, C., Yang, D., Yu, P., Chen, M.: Structural diversity for privacy in publishing social networks. In: Proc. of SDM (2011)
Zhou, B., Pei, J.: The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowledge and Information Systems 28(1), 47–77 (2011)
Zhou, B.: Preserving privacy in social networks against neighborhood attacks. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 506–515. IEEE (2008)
Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 93–106. ACM (2008)
Hay, M., Miklau, G., Jensen, D., Weis, P., Srivastava, S.: Anonymizing social networks. Computer Science Department Faculty Publication Series, p. 180 (2007)
Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: Proceedings of the 2008 SIAM International Conference on Data Mining (SDM 2008), pp. 739–750. Citeseer (2008)
Liu, L., Wang, J., Liu, J., Zhang, J.: Privacy preservation in social networks with sensitive edge weights. In: 2009 SIAM International Conference on Data Mining (SDM 2009), pp. 954–965. Sparks, Nevada (2009)
Zou, L., Chen, L., Özsu, M.: K-automorphism: A general framework for privacy preserving network publication. Proceedings of the VLDB Endowment 2(1), 946–957 (2009)
Cheng, J., Fu, A., Liu, J.: K-isomorphism: privacy preserving network publication against structural attacks. In: Proceedings of the 2010 International Conference on Management of Data, pp. 459–470. ACM (2010)
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Yu, L., Wang, Y., Wu, Z., Zhu, J., Hu, J., Chen, Z. (2014). Edges Protection in Multiple Releases of Social Network Data. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_71
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DOI: https://doi.org/10.1007/978-3-319-08010-9_71
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
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