Abstract
Recently, many works studied how to publish privacy preserving social networks for ”safely” data mining or analysis. These works all assume that there exists a single publisher who holds the complete graph. While, in real life, people join different social networks for different purposes. As a result, there are a group of publishers and each of them holds only a subgraph. Since no one has the complete graph, it is a challenging problem to generate the published graph in a distributed environment without releasing any publisher’s local content. In this paper, we propose a SMC (Secure Multi-Party Computation) based protocol to publish a privacy preserving graph in a distributed environment. Our scheme can publish a privacy preserving graph without leaking the local content information and meanwhile achieve the maximum graph utility. We show the effectiveness of the protocol on a real social network under different distributed storage cases.
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References
Bhagat, S., Cormode, G., Krishnamurthy, B., Srivastava, D.: Class-based graph anonymization for social network data. Proc. VLDB Endow. 2, 766–777 (2009)
Cheng, J., Fu, A.W.-C., Liu, J.: K-isomorphism: privacy preserving network publication against structural attacks. In: SIGMOD 2010, pp. 459–470. ACM, New York (2010)
Cormode, G., Srivastava, D., Yu, T., Zhang, Q.: Anonymizing bipartite graph data using safe groupings. Proc. VLDB Endow. 1, 833–844 (2008)
Frikken, K.B., Golle, P.: Private social network analysis: how to assemble pieces of a graph privately. In: WPES 2006, pp. 89–98. ACM, New York (2006)
Ganta, S.R., Kasiviswanathan, S., Smith, A.: Composition attacks and auxiliary information in data privacy. CoRR (2008)
Garg, S., Gupta, T., Carlsson, N., Mahanti, A.: Evolution of an online social aggregation network: an empirical study. In: IMC 2009, pp. 315–321. ACM, New York (2009)
Hay, M., Miklau, G., Jensen, D., Towsley, D., Weis, P.: Resisting structural re-identification in anonymized social networks. Proc. VLDB Endow. 1, 102–114 (2008)
Kerschbaum, F., Schaad, A.: Privacy-preserving social network analysis for criminal investigations. In: WPES 2008, pp. 9–14. ACM, New York (2008)
Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: SIGMOD 2008, pp. 93–106 (2008)
Yao, A.C.: Protocols for secure computations. In: SFCS 1982, pp. 160–164. IEEE Computer Society, Washington, DC (1982)
Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: SDM 2008 (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)
Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: ICDE 2008, pp. 506–515 (2008)
Zou, L., Chen, L., Özsu, M.T.: k-automorphism: a general framework for privacy preserving network publication. Proc. VLDB Endow. 2, 946–957 (2009)
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Yuan, M., Chen, L., Yu, P.S., Mei, H. (2013). Privacy Preserving Graph Publication in a Distributed Environment. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_10
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DOI: https://doi.org/10.1007/978-3-642-37401-2_10
Publisher Name: Springer, Berlin, Heidelberg
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