The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - Jan. (2014 vol.26)
pp: 235-252
Chih-Hua Tai , National Taipei University, Taiwan
Philip S. Yu , University of Illinois, Chicago
De-Nian Yang , Academia Sinica, Taipei
Ming-Syan Chen , Academia Sinica, Taipei and National Taiwan University, Taiwan
ABSTRACT
As an increasing number of social networking data is published and shared for commercial and research purposes, privacy issues about the individuals in social networks have become serious concerns. Vertex identification, which identifies a particular user from a network based on background knowledge such as vertex degree, is one of the most important problems that have been addressed. In reality, however, each individual in a social network is inclined to be associated with not only a vertex identity but also a community identity, which can represent the personal privacy information sensitive to the public, such as political party affiliation. This paper first addresses the new privacy issue, referred to as community identification, by showing that the community identity of a victim can still be inferred even though the social network is protected by existing anonymity schemes. For this problem, we then propose the concept of structural diversity to provide the anonymity of the community identities. The $(k)$-Structural Diversity Anonymization ($(k)$-SDA) is to ensure sufficient vertices with the same vertex degree in at least $(k)$ communities in a social network. We propose an Integer Programming formulation to find optimal solutions to $(k)$-SDA and also devise scalable heuristics to solve large-scale instances of $(k)$-SDA from different perspectives. The performance studies on real data sets from various perspectives demonstrate the practical utility of the proposed privacy scheme and our anonymization approaches.
INDEX TERMS
Communities, Social network services, Privacy, Data privacy, Couplings, Knowledge engineering, Cultural differences,anonymization, Social network, privacy
CITATION
Chih-Hua Tai, Philip S. Yu, De-Nian Yang, Ming-Syan Chen, "Structural Diversity for Resisting Community Identification in Published Social Networks", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 1, pp. 235-252, Jan. 2014, doi:10.1109/TKDE.2013.40
REFERENCES
[1] L. Backstrom, C. Dwork, and J.M. Kleinberg, "Wherefore Art Thou r3579x?: Anonymized Social Networks, Hidden Patterns, and Structural Steganography," Proc. 16th Int'l Conf. World Wide Web (WWW '07), 2007.
[2] A. Bettinelli, P. Hansen, and L. Liberti, "Algorithm for Parametric Community Detection in Networks," Physical Rev. E, vol. 86, article 016107, 2012.
[3] D. Chakrabarti, Y. Zhan, and C. Faloutsos, "R-MAT: A Recursive Model for Graph Mining," Proc. Fourth SIAM Int'l Conf. Data Mining (SDM '04), 2004.
[4] S. Chawla, C. Dwork, F. McSherry, A. Smith, and H. Wee, "Toward Privacy in Public Databases," Proc. Second Int'l Conf. Theory of Cryptography (TCC '05), 2005.
[5] J. Cheng, A.W. Fu, and J. Liu, "K-Isomorphism: Privacy Preserving Network Publication against Structural Attacks," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2010.
[6] S. Chester and G. Srivastava, "Social Network Privacy for Attribute Disclosure Attacks," Proc. Int'l Conf. Advances in Social Networks Analysis and Mining (ASONAM '11), 2011.
[7] A. Clauset, M.E.J. Newman, and C. Moore, "Finding Community Structure in Very Large Networks," Physical Rev. E., vol. 70, no. 6,article 066111, pp. 1-6, 2004.
[8] C. Dwork, "Differential Privacy: A Survey of Results," Proc. Fifth Int'l Conf. Theory and Applications of Models of Computation (TAMC '08), 2008.
[9] B.C.M. Fung, K. Wang, R. Chen, and P.S. Yu, "Privacy-Preserving Data Publishing: A Survey of Recent Developments," ACM Computing Surveys, vol. 42, no. 4, pp. 14:1-14:53, 2010.
[10] A. Gupta, A. Roth, and J. Ullman, "Iterative Constructions and Private Data Release," Proc. Ninth Int'l Conf. Theory of Cryptography (TCC '12), 2012.
[11] M. Hay, C. Li, G. Miklau, and D. Jensen, "Accurate Estimation of the Degree Distribution of Private Networks," Proc. IEEE Ninth Int'l Conf. Data Mining (ICDM '09), 2009.
[12] M. Hay, G. Miklau, D. Jensen, D.F. Towsley, and P. Weis, "Resisting Structural Re-Identification in Anonymized Social Networks," Proc. VLDB Endowment, vol. 1, pp. 102-114, 2008.
[13] V. Karwa, S. Raskhodnikova, A. Smith, and G. Yaroslavtsev, "Private Analysis of Graph Structure," Proc. VLDB Endowment, vol. 4, no. 11, pp. 1146-1157, 2011.
[14] J. Leskovec, K.J. Lang, A. Dasgupta, and M.W. Mahoney, "Statistical Properties of Community Structure in Large Social and Information Networks," Proc. 17th Int'l Conf. World Wide Web (WWW '08), 2008.
[15] N. Li, T. Li, and S. Venkatasubramanian, "t-Closeness: Privacy beyond k-Anonymity and l-Diversity," Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE '07), 2007.
[16] J. Li, Y. Tao, and X. Xiao, "Preservation of Proximity Privacy in Publishing Numerical Sensitive Data," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2008.
[17] K. Liu and E. Terzi, "Towards Identity Anonymization on Graphs," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2008.
[18] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, "l-Diversity: Privacy beyond k-Anonymity," ACM Trans. Knowledge Discovery from Data, vol. 1, no. 1,article 3, 2007.
[19] M.E. Nergiz, M. Atzori, and C. Clifton, "Hiding the Presence of Individuals from Shared Databases," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2007.
[20] M.E. Nergiz, C. Clifton, and A.E. Nergiz, "Multirelational k-Anonymity," IEEE Trans. Knowledge & Data Eng., vol. 21, no. 8, pp. 1104-1117, Aug. 2009.
[21] P. Samarati and L. Sweeney, "Generalizing Data to Provide Anonymity When Disclosing Information," Proc. ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems (PODS '98), 1998.
[22] L. Sweeney, "k-Anonymity: A Model for Protecting Privacy," Int'l J. Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 557-570, 2002.
[23] C.-H. Tai, P.-J. Tseng, P.S. Yu, and M.-S. Chen, "Identity Protection in Sequential Releases of Dynamic Social Networks," Proc. IEEE Int'l Conf. Data Mining (ICDM '11), 2011.
[24] X. Wu, X. Ying, K. Liu, and L. Chen, A Survey of Privacy- Preservation of Graphs and Social Networks. Springer, 2010.
[25] X. Ying and X. Wu, "Randomizing Social Networks: A Spectrum Preserving Approach," Proc. SIAM Int'l Conf. Data Mining (SDM '08), 2008.
[26] L. Zhang and W. Zhang, "Edge Anonymity in Social Network Graphs," Proc. Int'l Conf. Computational Science and Technology (CSE '09), 2009.
[27] E. Zheleva and L. Getoor, "Preserving the Privacy of Sensitive Relationships in Graph Data," Proc. First ACM SIGKDD Int'l Conf. Privacy, Security, and Trust in KDD (PinKDD '07), 2007.
[28] B. Zhou and J. Pei, "Preserving Privacy in Social Networks against Neighborhood Attacks," Proc. IEEE 24th Int'l Conf. Data Eng. (ICDE '08), 2008.
[29] B. Zhou, J. Pei, and W. Luk, "A Brief Survey on Anonymization Techniques for Privacy Preserving Publishing of Social Network Data," SIGKDD Explorations, vol. 10, no. 2, pp. 12-22, 2008.
[30] L. Zou, L. Chen, and M.T. Özsu, "K-Automorphism: A General Framework for Privacy Preserving Network Publication," Proc. VLDB Endowment, vol. 2, pp. 946-957, 2009.
120 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool