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A Model for Assessing the Risk of Revealing Shared Secrets in Social Networks

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Advances in Computational Intelligence (IPMU 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 300))

Abstract

We introduce the problem of information which become sensitive when combined, named shared secrets, and we propose a model based on Choquet integral to assess the risk that an actor in a social network is able to combine all information available. Some examples are presented and discussed and future directions are outlined.

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References

  1. 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 

  2. Campan, A., Truta, T.M.: Data and Structural k-Anonymity in Social Networks. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds.) PinKDD 2008. LNCS, vol. 5456, pp. 33–54. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  3. Faigle, U., Grabisch, M.: A discrete choquet integral for ordered systems. CoRR, abs/1102.1340 (2011)

    Google Scholar 

  4. Jung, Y., Kim, M., Masoumzadeh, A., Joshi, J.: A survey of security issue in multi-agent systems. Artificial Intelligence Review, 1–22 (2011), doi:10.1007/s10462-011-9228-8

    Google Scholar 

  5. Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: Proceedings of IEEE International Conference on Data Engineering, pp. 106–115 (2007)

    Google Scholar 

  6. Liu, K., Das, K., Grandison, T., Kargupta, H.: Privacy-preserving data analysis on graphs and social networks

    Google Scholar 

  7. 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 

  8. Mesiar, R.: Fuzzy measures and integrals. Fuzzy Sets and Systems 156(3), 365–370 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13, 1010–1027 (2001)

    Article  Google Scholar 

  10. Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  11. Simmons, G.J.: How to (Really) Share a Secret. In: Goldwasser, S. (ed.) CRYPTO 1988. LNCS, vol. 403, pp. 390–448. Springer, Heidelberg (1990)

    Google Scholar 

  12. Sugeno, M.: Theory of fuzzy integrals and its applications. PhD thesis, Tokyo Institute of Technology (1974)

    Google Scholar 

  13. Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 571–588 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  14. Torra, V., Domingo-Ferrer, J.: Disclosure control methods and information loss for microdata, pp. 91–110. Elsevier (2001)

    Google Scholar 

  15. Torra, V., Narukawa, Y.: Modeling decisions - information fusion and aggregation operators. Springer (2007)

    Google Scholar 

  16. Wong, R.C., Li, J., Fu, A.W., Wang, K.: (α,k) Anonymity: An Enhanced k -Anonymity Model for Privacy-Preserving Data Publishing, pp. 754–759. ACM (2006)

    Google Scholar 

  17. 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)

    Chapter  Google Scholar 

  18. Ying, X., Wu, X.: On link privacy in randomizing social networks. Knowl. Inf. Syst. 28(3), 645–663 (2011)

    Article  Google Scholar 

  19. 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)

    MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Engineering, University of Sannio, I-82100, Benevento, Italy

    Luigi Troiano

  2. Dept. of Computer Science, University of Oviedo, E-33204, Oviedo, Spain

    Irene Díaz

  3. Dept. of Statistics and O.R, University of Oviedo, E-33204, Oviedo, Spain

    Luis J. Rodríguez-Muñiz

Authors
  1. Luigi Troiano
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  2. Irene Díaz
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  3. Luis J. Rodríguez-Muñiz
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco  & Benedetto Matarazzo  & 

  2. CNRS UMR 7606, DAPA, LIP6 8, Université Pierre et Marie Curie - Paris6, rue du Capitaine Scott, F-75015, Paris, France

    Bernadette Bouchon-Meunier

  3. Dip. Matematica e Informatica, Università di Perugia, 06123, Perugia, Italy

    Giulianella Coletti

  4. Department of Computer and Management Science, University of Trento, Via Inama 5, 38122, Trento, Italy

    Mario Fedrizzi

  5. Machine Intelligence Institute — IONA College,, 10801, New Rochelle, NY, USA

    Ronald R. Yager

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© 2012 Springer-Verlag Berlin Heidelberg

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Troiano, L., Díaz, I., Rodríguez-Muñiz, L.J. (2012). A Model for Assessing the Risk of Revealing Shared Secrets in Social Networks. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_52

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  • DOI: https://doi.org/10.1007/978-3-642-31724-8_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31723-1

  • Online ISBN: 978-3-642-31724-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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