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Privacy Preserving Network Analysis of Distributed Social Networks

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Book cover Information Systems Security (ICISS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10063))

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Abstract

Social network analysis as a technique has been applied to a diverse set of fields, including, organizational behavior, sociology, economics and biology. However, for sensitive networks such as hate networks, trust networks and sexual networks, these techniques have been sparsely used. This is majorly attributed to the unavailability of network data. Anonymization is the most commonly used technique for performing privacy preserving network analysis. The process involves the presence of a trusted third party, who is aware of the complete network, and releases a sanitized version of it. In this paper, we propose an alternative, in which, the desired analysis can be performed by the parties who distributedly hold the network, such that: (a) no central third party is required; (b) the topology of the underlying network is kept hidden. We design multiparty protocols for securely performing few of the commonly studied social network analysis algorithms, which include degree distribution, closeness centrality, PageRank algorithm and K-shell decomposition algorithm. The designed protocols are proven to be secure in the presence of an arithmetic black-box extended with comparison, equality and modulo operations.

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References

  1. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  2. Barrat, A., Weigt, M.: On the properties of small-world network models. Eur. Phys. J. B-Condens. Matter Complex Syst. 13(3), 547–560 (2000)

    Article  Google Scholar 

  3. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. National Acad. Sci. 99(12), 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Borgatti, S.P., Everett, M.G.: Models of core/periphery structures. Soc. Netw. 21(4), 375–395 (2000)

    Article  Google Scholar 

  5. Eguiluz, V.M., Chialvo, D.R., Cecchi, G.A., Baliki, M., Apkarian, A.V.: Scale-free brain functional networks. Phys. Rev. Lett. 94(1), 018102 (2005)

    Article  Google Scholar 

  6. Kim, Y., Choi, T.Y., Yan, T., Dooley, K.: Structural investigation of supply networks: a social network analysis approach. J. Oper. Manage. 29(3), 194–211 (2011)

    Article  Google Scholar 

  7. Easley, D., Kleinberg, J., et al.: Networks, crowds, and markets: reasoning about a highly connected world. Significance 9, 43–44 (2012)

    Article  MATH  Google Scholar 

  8. Liljeros, F., Giesecke, J., Holme, P.: The contact network of inpatients in a regional healthcare system. a longitudinal case study. Math. Popul. Stud. 14(4), 269–284 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  9. Rocha, L.E., Liljeros, F., Holme, P.: Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Comput. Biol. 7(3), e1001109 (2011)

    Article  Google Scholar 

  10. Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Preventing private information inference attacks on social networks. IEEE Trans. Knowl. Data Eng. 25(8), 1849–1862 (2013)

    Article  Google Scholar 

  11. Tassa, T., Cohen, D.J.: Anonymization of centralized and distributed social networks by sequential clustering. IEEE Trans. Knowl. Data Eng. 25(2), 311–324 (2013)

    Article  Google Scholar 

  12. Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 173–187. IEEE (2009)

    Google Scholar 

  13. Narayanan, A., Shi, E., Rubinstein, B.I.: Link prediction by de-anonymization: How we won the kaggle social network challenge. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1825–1834. IEEE (2011)

    Google Scholar 

  14. Bhaskar, R., Laxman, S., Smith, A., Thakurta, A.: Discovering frequent patterns in sensitive data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 503–512. ACM (2010)

    Google Scholar 

  15. Kleinberg, J.M.: Challenges in mining social network data: processes, privacy, and paradoxes. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 4–5. ACM (2007)

    Google Scholar 

  16. Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy, SP 2008, pp. 111–125. IEEE (2008)

    Google Scholar 

  17. Xue, M., Karras, P., Chedy, R., Kalnis, P., Pung, H.K.: Delineating social network data anonymization via random edge perturbation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 475–484. ACM (2012)

    Google Scholar 

  18. Fard, A.M., Wang, K.: Neighborhood randomization for link privacy in social network analysis. World Wide Web 18(1), 9–32 (2015)

    Article  Google Scholar 

  19. Hogg, T., Adamic, L.: Enhancing reputation mechanisms via online social networks. In: Proceedings of the 5th ACM Conference on Electronic Commerce, pp. 236–237. ACM (2004)

    Google Scholar 

  20. Yao, A.C.C.: Protocols for secure computations. FOCS 82, 160–164 (1982)

    MathSciNet  Google Scholar 

  21. Brickell, J., Shmatikov, V.: Privacy-preserving graph algorithms in the semi-honest model. In: Roy, B. (ed.) ASIACRYPT 2005. LNCS, vol. 3788, pp. 236–252. Springer, Heidelberg (2005). doi:10.1007/11593447_13

    Chapter  Google Scholar 

  22. Aly, A., Cuvelier, E., Mawet, S., Pereira, O., Vyve, M.: Securely solving simple combinatorial graph problems. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 239–257. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39884-1_21

    Chapter  Google Scholar 

  23. Aly, A., Vyve, M.: Securely solving classical network flow problems. In: Lee, J., Kim, J. (eds.) ICISC 2014. LNCS, vol. 8949, pp. 205–221. Springer, Heidelberg (2015). doi:10.1007/978-3-319-15943-0_13

    Google Scholar 

  24. Blanton, M., Steele, A., Alisagari, M.: Data-oblivious graph algorithms for secure computation and outsourcing. In: Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security, pp. 207–218. ACM (2013)

    Google Scholar 

  25. Frikken, K.B., Golle, P.: Private social network analysis: how to assemble pieces of a graph privately. In: Proceedings of the 5th ACM Workshop on Privacy in Electronic Society, pp. 89–98. ACM (2006)

    Google Scholar 

  26. Kukkala, V.B., Iyengar, S., Saini, J.S.: Secure multiparty graph computation. In: 2016 8th International Conference on Communication Systems and Networks (COMSNETS), pp. 1–2. IEEE (2016)

    Google Scholar 

  27. Kerschbaum, F., Schaad, A.: Privacy-preserving social network analysis for criminal investigations. In: Proceedings of the 7th ACM Workshop on Privacy in the Electronic Society, pp. 9–14. ACM (2008)

    Google Scholar 

  28. Fridgen, G., Garizy, T.Z.: Supply chain network risk analysis - A privacy preserving approach. In: 23rd European Conference on Information Systems, ECIS 2015, Münster, Germany, 26–29 May 2015

    Google Scholar 

  29. Tassa, T., Bonchi, F.: Privacy preserving estimation of social influence. In: EDBT, pp. 559–570 (2014)

    Google Scholar 

  30. Cramer, R., Damgard, I., Nielsen, J.B.: Secure multiparty computation and secret sharing-an information theoretic appoach. Book Draft (2012)

    Google Scholar 

  31. Ben-Or, M., Goldwasser, S., Wigderson, A.: Completeness theorems for non-cryptographic fault-tolerant distributed computation. In: Proceedings of the Twentieth Annual ACM Symposium on Theory of Computing, pp. 1–10. ACM (1988)

    Google Scholar 

  32. Paillier, P.: Public-Key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). doi:10.1007/3-540-48910-X_16

    Google Scholar 

  33. Damgård, I., Fitzi, M., Kiltz, E., Nielsen, J.B., Toft, T.: Unconditionally secure constant-rounds multi-party computation for equality, comparison, bits and exponentiation. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 285–304. Springer, Heidelberg (2006). doi:10.1007/11681878_15

    Chapter  Google Scholar 

  34. Kukkala, V.B., Saini, J.S., Iyengar, S.: Privacy preserving network analysis of distributed social networks. Cryptology ePrint Archive, Report 2016/427 (2016). http://eprint.iacr.org/2016/427

  35. Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)

    Article  Google Scholar 

  36. Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  37. Dorogovtsev, S.N., Mendes, J.F.: Evolution of networks. Adv. Phys. 51(4), 1079–1187 (2002)

    Article  Google Scholar 

  38. Adamic, L.A., Huberman, B.A.: Power-law distribution of the world wide web. Science 287(5461), 2115–2115 (2000)

    Article  Google Scholar 

  39. Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: ACM SIGCOMM Computer Communication Review, vol. 29, pp. 251–262. ACM (1999)

    Google Scholar 

  40. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42. ACM (2007)

    Google Scholar 

  41. Bavelas, A.: Communication patterns in task-oriented groups. J. Acoust. Soc. Am. 22(6), 726–730 (1950)

    Article  Google Scholar 

  42. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, Cambridge (1994)

    Book  MATH  Google Scholar 

  43. Hsieh, C.S., Kovářík, J., Logan, T.: How central are clients in sexual networks created by commercial sex? Scientific reports 4 (2014)

    Google Scholar 

  44. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)

    Google Scholar 

  45. Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)

    Article  Google Scholar 

  46. Franceschet, M.: Pagerank: standing on the shoulders of giants. Commun. ACM 54(6), 92–101 (2011)

    Article  Google Scholar 

  47. Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nature Phys. 6(11), 888–893 (2010)

    Article  Google Scholar 

  48. Batagelj, V., Zaversnik, M.: An o (m) algorithm for cores decomposition of networks. arXiv preprint cs/0310049 (2003)

    Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India

    Varsha Bhat Kukkala, Jaspal Singh Saini & S. R. S. Iyengar

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  1. Varsha Bhat Kukkala
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  2. Jaspal Singh Saini
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  3. S. R. S. Iyengar
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Corresponding author

Correspondence to S. R. S. Iyengar .

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Editors and Affiliations

  1. Colorado State University, Fort Collins, Colorado, USA

    Indrajit Ray

  2. Malaviya National Institute of Technology, Jaipur, India

    Manoj Singh Gaur

  3. University of Padua, Padua, Italy

    Mauro Conti

  4. IIIT Delhi, Delhi, India

    Dheeraj Sanghi

  5. IIT Madras, Madras, India

    V. Kamakoti

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Kukkala, V.B., Saini, J.S., Iyengar, S.R.S. (2016). Privacy Preserving Network Analysis of Distributed Social Networks. In: Ray, I., Gaur, M., Conti, M., Sanghi, D., Kamakoti, V. (eds) Information Systems Security. ICISS 2016. Lecture Notes in Computer Science(), vol 10063. Springer, Cham. https://doi.org/10.1007/978-3-319-49806-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-49806-5_18

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