Most social networks allow individuals to share their information with friends but also with unknown people. Therefore, in order to prevent unauthorized access to sensitive, private information, the study of privacy issues in social networks has become an important task. This paper provides a brief overview of the emerging research in privacy issues in social networks.


Link Privacy data mining social networks 


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  1. 1.
    Adamic, L.A., Adar, E.: Friends and neighbors on the web. Social networks 25(3), 211–230 (2003)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C.C., Yu, P.S.: An introduction to privacy-preserving data mining. In: Privacy-Preserving Data Mining, pp. 1–9 (2008)Google Scholar
  3. 3.
    Beach, A., Gartrell, M., Han, R.: q-anon: Rethinking anonymity for social networks. In: Elmagarmid, A.K., Agrawal, D. (eds.) SocialCom/PASSAT, pp. 185–192. IEEE Computer Society (2010)Google Scholar
  4. 4.
    Beach, A., Gartrell, M., Han, R.: Social-K: Real-time K-anonymity guarantees for social network applications. In: 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 600–606. IEEE ( March 2010)Google Scholar
  5. 5.
    Cartwright, D.: Achieving change in people: Some applications of group dynamics theory. Human Relations 4(4), 381–392 (1951)CrossRefGoogle Scholar
  6. 6.
    Cartwright, D., Harary, F.: Structural balance: a generalization of heider’s theory. Psychological Review 63(5), 277 (1956)CrossRefGoogle Scholar
  7. 7.
    Deutsch, K.W.: On communication models in the social sciences. Public Opinion Quarterly 16(3), 356–380 (1952)CrossRefGoogle Scholar
  8. 8.
    Díaz, I., Ranilla, J., Rodríguez-Muniz, L.J., Troiano, L.: Identifying the Risk of Attribute Disclosure by Mining Fuzzy Rules. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. CCIS, vol. 80, pp. 455–464. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Díaz, I., Rodríguez-Muñiz, L.J., Troiano, L.: Fuzzy sets in data protection: strategies and cardinalities. Logic Journal of IGPL (2011)Google Scholar
  10. 10.
    Domingo-Ferrer, J., Torra, V.: On the connections between statistical disclosure control for microdata and some artificial intelligence tools. Inf. Sci. Inf. Comput. Sci. 151, 153–170 (2003)zbMATHGoogle Scholar
  11. 11.
    Dwork, C.: Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Dwork, C.: Differential Privacy: A Survey of Results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Dwyer, C., Hiltz, S.R., Passerini, K.: Trust and privacy concern within social networking sites: A comparison of facebook and myspace. In: Proceedings of the Thirteenth Americas Conference on Information Systems (AMCIS 2007) (2007) Paper 339Google Scholar
  14. 14.
    Fang, C., Kohram, M., Ralescu, A.: Towards a spectral regression with low-rank approximation approach for link prediction in dynamic graphs. IEEE Intelligent Systems 99, 1Google Scholar
  15. 15.
    Hanneman, R.A., Riddle, M.: Introduction to social network methods. University of California, Riverside (2005)Google Scholar
  16. 16.
    Heider, F.: Attitudes and cognitive organization. The Journal of Psychology 21(1), 107–112 (1946)CrossRefGoogle Scholar
  17. 17.
    Holsheimer, M., Siebes, A.P.J.M.: Data mining: the search for knowledge in databases. Technical report, Amsterdam, The Netherlands, The Netherlands (1994)Google Scholar
  18. 18.
    Jamali, M., Abolhassani, H.: Different aspects of social network analysis. In: IEEE/WIC/ACM International Conference on Web Intelligence, WI 2006, pp. 66–72 (December 2006)Google Scholar
  19. 19.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)zbMATHCrossRefGoogle Scholar
  20. 20.
    Korolova, A., Motwani, R., Nabar, S.U., Xu, Y.: Link privacy in social networks. In: Proceeding of the 17th ACM Conference on Information and Knowledge Management, pp. 289–298. ACM (2008)Google Scholar
  21. 21.
    Laird, P.D.: Learning from good and bad data. Kluwer Academic Publishers, Norwell (1988)zbMATHCrossRefGoogle Scholar
  22. 22.
    Leicht, E.A., Holme, P., Newman, M.E.J.: Vertex similarity in networks. Physical Review E 73(2), 26120 (2006)CrossRefGoogle Scholar
  23. 23.
    Leontief, W.W.: The structure of American economy, 1919-1939: An empirical application of equilibrium analysis. Oxford University Press, New York (1951)Google Scholar
  24. 24.
    Li, N., Li, T.: t-closeness: Privacy beyond k-anonymity and?-diversity. In: Proceedings of IEEE International Conference on Data Engineering (2007)Google Scholar
  25. 25.
    Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE, pp. 106–115 (2007)Google Scholar
  26. 26.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. Journal of the American society for information science and technology 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  27. 27.
    Liu, K., Das, K., Grandison, T., Kargupta, H.: Privacy-preserving data analysis on graphs and social networksGoogle Scholar
  28. 28.
    Loukides, G., Shao, J.: Preventing range disclosure in k-anonymised data. Expert Syst. Appl. 38(4), 4559–4574 (2011)CrossRefGoogle Scholar
  29. 29.
    Lovász, L.: Random walks on graphs: A survey. Combinatorics, Paul Erdos is Eighty 2(1), 1–46 (1993)Google Scholar
  30. 30.
    Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. In: 22nd IEEE International Conference on Data Engineering (2006)Google Scholar
  31. 31.
    Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13, 1010–1027 (2001)CrossRefGoogle Scholar
  32. 32.
    Sarathy, R., Muralidhar, K.: Evaluating laplace noise addition to satisfy differential privacy for numeric data. Transactions on Data Privacy 4(1), 1–17 (2011)MathSciNetGoogle Scholar
  33. 33.
  34. 34.
    Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  35. 35.
    Ying, X., Wu, X.: On Link Privacy in Randomizing Social Networks. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 28–39. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  36. 36.
    Zheleva, E., Getoor, L.: To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In: Proceedings of the 18th International Conference on World Wide Web, pp. 531–540. ACM (2009)Google Scholar
  37. 37.
    Zhou, B., Pei, J.: The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowledge and Information Systems 28(1), 1–38 (2010)MathSciNetGoogle Scholar
  38. 38.
    Zhou, B., Pei, J., Luk, W.: A brief survey on anonymization techniques for privacy preserving publishing of social network data. SIGKDD Explor. Newsl. 10, 12–22 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Irene Díaz
    • 1
  • Anca Ralescu
    • 2
  1. 1.Department of Computer ScienceUniversity of OviedoOviedoSpain
  2. 2.School of Computing Sciences & Informatics, Machine Learning & Computational Intelligence LabUniversity of CincinnatiCincinnatiUSA

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