Abstract
Recently, people share their information via social platforms such as Facebook and Twitter in their daily life. Social networks on the Internet can be regarded as a microcosm of the real world and worth being analyzed. Since the data in social networks can be private and sensitive, privacy preservation in social networks has been a focused study. Previous works develop anonymization methods for a single social network represented by a single graph, which are not enough for the analysis on the evolution of the social network. In this paper, we study the privacy preserving problem considering the evolution of a social network. A time-series of social network graphs representing the evolution of the corresponding social network are anonymized to a sequence of sanitized graphs to be released for further analysis. We point out that naively applying the existing approaches to each time-series graph will break the privacy purposes, and propose an effective anonymization method extended from an existing approach, which takes into account the effect of time for releasing multiple anonymized graphs at one time. We use two real datasets to test our method and the experiment results demonstrate that our method is very effective in terms of data utility for query answering.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bhagat, S., Cormode, G., Krishnamurthy, B., Srivastava, D.: Class-Based Graph Anonymization for Social Network Data. In: Proceedings of the 35th International Conference on Very Large Data Base, pp. 766–777 (2009)
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 (2007)
Cormode, G., Srivastava, D., Yu, T., Zhang, Q.: Anonymizing Bipartite Graph Data Using Safe Groupings. In: Proceedings of the 34th International Conference on Very Large Data Base, pp. 833–844 (2008)
Liu, K., Das, K., Grandison, T., Kargupta, H.: Privacy-Preserving Data Analysis on Graphs and Social Networks. In: Kargupta, H., Han, J., Yu, P., Motwani, R., Kumar, V. (eds.) Next Generation Data Mining, ch. 21, pp. 419–437. CRC Press (December 2008)
Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M.: Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. University of Massachusetts Technical Report, Internet Mathematics 6(1), 29–123 (2009)
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 (2008)
Mislove, A., Koppula, H.S., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Growth of the Flickr Social Network. In: Proceedings of the 1th ACM SIGCOMM Workshop on Online Social Networks, pp. 25–30 (2008)
Wu, X., Ying, X., Liu, K., Chen, L.: A Survey of Privacy-Preservation of Graphs and Social Networks. In: Aggarwal, C.C., Wang, H. (eds.) Managing and Mining Graph Data, vol. 40, pp. 421–453. Springer US (2010)
Yuan, M., Chen, L., Yu, P.: Personalized Privacy Protection in Social Networks. In: Proceedings of the 37th International Conference on Very Large Data Base, pp. 141–150 (2010)
Zou, L., Chen, L., Ozsu, M.T.: K-automorphism: A General Framework for Privacy Preserving Network Publication. In: Proceedings of the 35th International Conference on Very Large Data Base, pp. 946–957 (2009)
Zhou, B., Pei, J.: Preserving Privacy in Social Networks against Neighborhood Attacks. In: Proceedings of the 24th IEEE International Conference on Data Engineering, pp. 506–515 (2008)
Zhou, B., Pei, J., Luk, W.-S.: A Brief Survey on Anonymization Techniques for Privacy Preserving Publishing of Social Network Data. In: Proceedings of the SIGKDD Explorations, pp. 12–22 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, CJ.L., Wang, E.T., Chen, A.L.P. (2013). Anonymization for Multiple Released Social Network Graphs. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-37456-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37455-5
Online ISBN: 978-3-642-37456-2
eBook Packages: Computer ScienceComputer Science (R0)