Abstract
We propose a method for network anonymization that consists on sampling a subset of vertices and merging its neighborhoods in the network. In such a way, by publishing the merged graph of the network together with the sampled vertices and their locally anonymized neighborhoods, we obtain a complete anonymized picture of the network. We prove that the anonymization of the merged graph incurs in lower information loss, hence, it has more utility than the direct anonymization of the graph. It also yields an improvement on the quality of the anonymization of the local neighbors of a given subset of vertices.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adamic, L.A., Glance, N.: The political blogosphere and the 2004 US election. In: Proceedings of the WWW-2005 Workshop on the Weblogging Ecosystem (2005)
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)
Backstrom, L., Dwork, C., Kleinberg, J.: Where art thou R3579X? Anonymized social networks, hidden patterns, and structural steganography. In: Proceedings of 16th International World Wide Web Conference (2007)
Campan, A., Truta, T.M.: A clustering approach for data and structural anonymity in social networks. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2008), in conjunction with KDD 2008, Las Vegas, Nevada, USA (2008)
Campan, A., Truta, T.M.: Data and structural k-anonymity in social networks. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds.) PinKDD 2008. LNCS, vol. 5456, pp. 33–54. Springer, Heidelberg (2009)
Chester, S., Kapron, B.M., Ramesh, G., Srivastava, G., Thomo, A., Venkatesh, S.: Why Waldo befriended the dummy? k-Anonymization of social networks with pseudo-nodes. Soc. Netw. Anal. Min. 3(3), 381–399 (2013)
Chester, S., Kapron, B., Srivastava, G., Venkatesh, S.: Complexity of social network anonymization. Soc. Netw. Anal. Min. 3(2), 151–166 (2013)
Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Trans. Knowl. Data Eng. 14(1), 189–201 (2002)
Domingo-Ferrer, J., Torra, V.: Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Min. Knowl. Discov. 11(2), 195–212 (2005)
Freeman, L.C.: Centrality in social networks: conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)
Hay, M., Miklau, G., Jensen, D., Towsley, D.: Resisting structural identification in anonymized social networks. In: Proceedings of the 34th International Conference on Very Large Databases (VLDB 2008). ACM (2008)
Hansen, S.L., Mukherjee, S.: A polynomial algorithm for optimal univariate microaggregation. IEEE Trans. Knowl. Data Eng. 15(4), 1043–1044 (2003)
Krebs, V.: (unpublished). http://www.orgnet.com/
Krishnamurthy, V., Faloutsos, M., Chrobak, M., Cui, J., Lao, L., Percus, A.: Sampling large internet topologies for simulation purposes. Comput. Netw. 51(15), 4284–4302 (2007)
Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 93–106 (2008)
Nettleton, D.F., Dries, A.: Local neighbourhood sub-graph matching method, European Patent application number: 13382308.8 (Priority 30/7/2013). PCT application number: PCT/ES2014/065505 (Priority 18 July 2014)
Nettleton, D.F., Salas, J.: A data driven anonymization system for information rich online social network graphs. Expert Syst. Appl. 55, 87–105 (2016)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Preprint Physics/0605087 (2006)
Samarati, P.: Protecting respondents identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)
Salas, J., Torra, V.: Graphic sequences, distances and k-degree anonymity. Disc. Appl. Math. 188, 25–31 (2015)
Salas, J., Torra, V.: Improving the characterization of P-stability for applications in network privacy. Disc. Appl. Math. 206, 109–114 (2016)
Stokes, K., Torra, V.: Reidentification and k-anonymity: a model for disclosure risk in graphs. Soft Comput. 16(10), 1657–1670 (2012)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)
Truta, T.M., Campan, A., Ralescu, A.L.: Preservation of structural properties in anonymized social networks. In: CollaborateCom, pp. 619–627 (2012)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)
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)
Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: ICDE (2008)
Zhou, B., Pei, J., Luk, W.S.: A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM SIGKDD Explor. Newsl. 10(2), 12–22 (2008)
Acknowledgements
Support by Spanish MCYT under project SmartGlacis TIN2014-57364-C2-1-R is acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Salas, J. (2016). Sampling and Merging for Graph Anonymization. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2016. Lecture Notes in Computer Science(), vol 9880. Springer, Cham. https://doi.org/10.1007/978-3-319-45656-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-45656-0_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45655-3
Online ISBN: 978-3-319-45656-0
eBook Packages: Computer ScienceComputer Science (R0)