Abstract
Weighted social network has a broad usage in the data mining fields, such as collaborative filtering, influence analysis, phone log analysis, etc. However, current privacy models which prevent node re-identification for the social network only dealt with unweighted graphs. In this paper, we make use of the special characteristic of edge weights to define a novel k-weighted-degree anonymous model. While keeping the weight utilities, this model helps prevent node re-identification in the weighted graph based on three distance functions which measure the nodes’ difference. We also design corresponding algorithms for each distance to achieve anonymity. Some experiments on real datasets show the effectiveness of our methods.
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
Aggarwal, G., Feder, T., Kenthapadi, K., Khuller, S., Panigrahy, R., Thomas, D., Zhu, A.: Achieving Anonymity via Clustering. In: PODS 2006, pp. 153–162 (2006)
Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: WWW 2007, pp. 181–190 (2007)
Bhagat, S., Cormode, G., Krishnamurthy, B., Srivastava, D.: Class-based graph anonymization for social network data. Proc. VLDB Endow. 2(1), 766–777 (2009)
Campan, A., Truta, T.M.: A clustering approach for data and structural anonymity in social networks. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds.) PinKDD 2008. LNCS, vol. 5456. Springer, Heidelberg (2008)
Cormode, G., Srivastava, D., Yu, T., Zhang, Q.: Anonymizing bipartite graph data using safe groupings. Proc. VLDB Endow. 1(1), 833–844 (2008)
Cheng, J., Fu, A., Liu, J.: K-Isomorphism: Privacy Preserving Network Publication against Structural Attacks. In: SIGMOD 2010, pp. 459–470 (2010)
Das, S., Egecioglu, O., Abbadi, A.: Privacy Preserving in Weighted Social Network. In: ICDE 2010, pp. 904–907 (2010)
Hay, M., Miklau, G., Jensen, D., Towsley, D., Weis, P.: Resisting structural re-identification in anonymized social networks. Proc. VLDB Endow. 1(1), 102–114 (2008)
Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: SIGMOD 2008, pp. 93–106 (2008)
Liu, L., Wang, J., Liu, J., Zhang, J.: Privacy preserving in social networks against sensitive edge disclosure. Technical Report CMIDA-HiPSCCS 006-08 (2008)
Li, N., Li, T.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE 2007, pp. 106–115 (2007)
Shrivastava, N., Majumder, A., Rastogi, R.: Mining (social) network graphs to detect random link attacks. In: ICDE 2008, pp. 486–495 (2008)
Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: Jonker, W., Petković, M. (eds.) SDM 2008. LNCS, vol. 5159, pp. 739–750. Springer, Heidelberg (2008)
Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: ICDE 2008, pp. 506–515 (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)
Zou, L., Chen, L., Özsu, M.T.: k-automorphism: a general framework for privacy preserving network publication. Proc. VLDB Endow. 2(1), 946–957 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yuan, M., Chen, L. (2011). Node Protection in Weighted Social Networks. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20149-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-20149-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20148-6
Online ISBN: 978-3-642-20149-3
eBook Packages: Computer ScienceComputer Science (R0)