Abstract
Privacy preserving data publication is an emerging trend in data publication that focuses on the dual concerns: information privacy and utility. Privacy preservation is essential in social networks as social networks are abundant source of information relating to the behavior of social entities. Social network disseminates its information through social graph. In this paper, we propose a new attack model based on centrality measures. The attack model focuses on identity disclosure problem. Adversary is supplied with centrality measure information of original social graph, which he uses to de-anonymize the published anonymous graph. We have proposed an anonymization model based on level-cut heuristic clustering to generate k-degree anonymous sequence. This step is followed by k-degree closeness anonymous graph construction derived from rich-get-richer phenomenon, transformation, and validation. It is found from the analysis that our proposed approach performs well in assortative networks.
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References
Aggarwal G, Feder T, Kenthapadi K, Motwani R, Panigrahy R, Thomas D, Zhu A (2005) Anonymizing tables. In: ICDT, 2005, pp 246–258
Alufasian Y, Campan A (2013) Preservation of centrality measures in anonymized social networks. In: SocialCom.2013.75, 2013, pp 486–493
Available as MATLAB mat-file at: https://www.cise.ufl.edu/research/sparse/matrices/Pajek/Kohonen.html
Barabasi A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512
Batageli V, Mrvar A (2006) Pajek datasets. Available in the name of CS phd at: http://vlado.fmf.uni-lj.si/pub/networks/data/
Chester S, Kapron BM, Ramesh G, Srivastava G, Thomo A, Venkatesh S (2011) k-anonymization of social networks by vertex addition. ADBIS (2) 789:107–116
Hann J, Kamber N (2001) Data mining: concepts and techniques. Morgan Kaufmann Publishers, San Francisco
Hay M, Miklau G, Jensen D, Weis P, Srivastava S (2007) Anonymizing social networks. Technical report, University of Massachusetts Amherst, 2007
Kapron B M, Srivastava G, Venkatesh S (2011) Social network anonymization via edge addition. In: ASONAM, 2011, pp 155–162
Lindell Y, Pinkas B (2009) Secure multiparty computation for privacy-preserving data mining. J Priv Confid 1(1):59–98
Liu K and Terzi E (2008) Towards identity anonymization on graphs. In: Proceedings of ACM SIGMOD, 2008, pp 93–106
Liu K, Das K, Grandison T, Kargupta H (2008) Privacy-preserving data analysis on graphs and social networks. In: Kargupta H, Han J, Yu P, Motwani R, Kumar V (eds) Next generation of data mining. Chapman & Hall, London, pp 419–437
Machanavajjhala A, GehrkeJ, Kifer D, Venkitasubramaniam M (2006) l-diversity: privacy beyond k-anonymity. In:Proceeding of 22nd ICDE, 2006, p 24
Maning CD, Raghavan P, Schütze H (2008) An introduction to information retrieval. Cambridge University Press, Cambridge
Meyerson A, Williams R (2004) On the complexity of optimal k-anonymity. In: PODS, 2004, pp 223–228
Mohapatra D, Patra MR (2015) k-degree closeness anonymity: a centrality measure based approach for network anonymization. In: Proceedings of ICDCIT, 2015, pp 299–310
Newman MEJ (2002) Assortative mixing in networks. Phys Rev Lett 89:208701
Pedarsani P, Grossglauser M (2011) On the privacy of anonymized networks. In: KDD(11), 2011, pp 1235–1243
Sweeney L (2002a) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 10(5):557–570
Sweeney L (2002b) Achieving k-anonymity privacy protec-tion using generalization and suppression. Int J Uncertain Fuzziness Knowl Based Syst 10(5):571–588
Tai CH, Tseng P-J, Yu PS, Chen M-S (2014) Identity protection in sequential releases of dynamic networks. IEEE Trans Knowl Data Eng 26(3):635–651
Wang G, Liu Q, Li F, Yang S, Wu J (2013) Outsourcing privacy-preserving social networks to cloud. In: INFOCOM 2013, pp 2886–2894
Wentao W, Yanghua X, Wei W, Zhenying H and Zhihui W (2010) K-symmetry model for identity anonymization in social networks. In: Proceedings of the 13th international conference on extending database technology (EDBT’10), 2010, pp 111–122
West DB (2009) Introduction to graph theory, 2nd edn. Prentice Hall, Upper saddle river
Wong RCW, Li J, Fu A W, Wang K (2006) (α,k) anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In: KDD’06, pp 754–759
Xiao X, Tao Y. Anatomy: Simple and Effective Privacy Preservation.VLDB, 2006, 139-150
Zhou B and Pei J (2008) Preserving privacy in social networks against neighborhood attacks. In: Proceedings of the 24th international conference on data engineering (ICDE’08), 2008, pp 506–515
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Mohapatra, D., Patra, M.R. A level-cut heuristic-based clustering approach for social graph anonymization. Soc. Netw. Anal. Min. 7, 50 (2017). https://doi.org/10.1007/s13278-017-0470-1
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DOI: https://doi.org/10.1007/s13278-017-0470-1