[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

DUEF-GA: data utility and privacy evaluation framework for graph anonymization

  • Regular Contribution
  • Published:
International Journal of Information Security Aims and scope Submit manuscript

Abstract

Anonymization of graph-based data is a problem which has been widely studied over the last years, and several anonymization methods have been developed. Information loss measures have been used to evaluate data utility and information loss in the anonymized graphs. However, there is no consensus about how to evaluate data utility and information loss in privacy-preserving and anonymization scenarios, where the anonymous datasets were perturbed to hinder re-identification processes. Authors use diverse metrics to evaluate data utility and, consequently, it is complex to compare different methods or algorithms in the literature. In this paper, we propose a framework to evaluate and compare anonymous datasets in a common way, providing an objective score to clearly compare methods and algorithms. Our framework includes metrics based on generic information loss measures, such as average distance or betweenness centrality and also task-specific information loss measures, such as community detection or information flow. Additionally, we provide some metrics to examine re-identification and risk assessment. We demonstrate that our framework could help researchers and practitioners to select the best parametrization and/or algorithm to reduce information loss and maximize data utility.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Available at: https://github.com/jcasasr/DUEF-GA.

References

  1. Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: WWW ’07, pp. 181–190. ACM, New York, NY, USA (2007)

  2. Barabási, A.-L., Pósfai, M.: Network Science. Cambridge University Press, Cambridge (2016)

    Google Scholar 

  3. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)

    Article  MATH  Google Scholar 

  4. Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)

    Article  MATH  Google Scholar 

  5. Brunet, S., Canard, S., Gambs, S., Olivier, B.: Novel differentially private mechanisms for graphs. IACR Cryptol. ePrint Arch. 2016, 745 (2016)

    Google Scholar 

  6. Bonchi, F., Gionis, A., Tassa, T.: Identity obfuscation in graphs through the information theoretic lens. Inf. Sci. 275, 232–256 (2014)

    Article  MathSciNet  Google Scholar 

  7. Cai, B.-J., Wang, H.-Y., Zheng, H.-R., Wang, H.: Evaluation repeated random walks in community detection of social networks. In: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), pp. 1849–1854. IEEE Computer Society, Qingdao (2010)

  8. Casas-Roma, J.: Privacy-preserving on graphs using randomization and edge-relevance. In: Torra, V. (ed.) International Conference on Modeling Decisions for Artificial Intelligence (MDAI), pp. 204–216. Springer International Publishing Switzerland, Tokyo, Japan (2014)

  9. Casas-Roma, J., Herrera-joancomartí, J., Torra, V.: Anonymizing graphs: measuring quality for clustering. Knowl. Inf. Syst. 44(3), 507–528 (2015)

    Article  Google Scholar 

  10. Casas-Roma, J., Herrera-joancomartí, J., Torra, V.: \(k\)-Degree anonymity and edge selection: improving data utility in large networks. Knowl. Inf. Syst. 50(2), 447–474 (2016)

    Google Scholar 

  11. Casas-Roma, J., Herrera-Joancomartí, J., Torra, V.: A survey of graph-modification techniques for privacy-preserving on networks. Artif. Intell. Rev. 47(3), 341–366 (2017)

    Article  Google Scholar 

  12. Chakrabarti, D., Faloutsos, C.: Graph mining: laws, generators, and algorithms. ACM Comput. Surv. 38(1), 2:1-2:69 (2006)

    Article  Google Scholar 

  13. Chester, S., Kapron, B.M., Ramesh, G., Srivastava, G., Thomo, A., Venkatesh, S.: \(k\)-Anonymization of social networks by vertex addition. In: ADBIS 2011 Research Communications, pp. 107–116. Vienna, Austria, CEUR-WS.org (2011)

  14. Chester, S., Kapron, B.M., Ramesh, G., Srivastava, G., Thomo, A., Venkatesh, S.: Why Waldo befriended the dummy? \(k\)-Anonymization of social networks with pseudo-nodes. Soc. Netw. Anal. Min. 3(3), 381–399 (2013)

    Google Scholar 

  15. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 66111 (2004)

    Article  Google Scholar 

  16. Dwork, C.: Differential privacy. In: Proceedings of the 33rd International Conference on Automata, Languages and Programming (ICALP), pp 1–12. Springer-Verlag, Berlin (2006)

  17. Ferri, F., Grifoni, P., Guzzo, T.: New forms of social and professional digital relationships: the case of Facebook. Soc. Netw. Anal. Min. 2(2), 121–137 (2011)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  19. Guimerà, R., Danon, L., Díaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 65–103 (2003)

    Article  Google Scholar 

  20. Hay, M., Miklau, G., Jensen, D., Weis, P., Srivastava, S.: Anonymizing social networks. Report, University of Massachusetts Amherst (2007)

  21. Hay, M., Miklau, G., Jensen, D., Towsley, D., Weis, P.: Resisting structural re-identification in anonymized social networks. Proc. VLDB Endow. 1(1), 102–114 (2008)

    Article  Google Scholar 

  22. Hay, M., Li, C., Miklau, G., Jensen, D.: Accurate estimation of the degree distribution of private networks. In: IEEE International Conference on Data Mining (ICDM), pp. 169–178. IEEE Computer Society, Miami, FL (2009)

  23. Hay, M., Liu, K., Miklau, G., Pei, J., and Terzi, E. (2011). Privacy-aware data management in information networks. In: International Conference on Management of Data (SIGMOD). ACM Press, New York, USA, pp. 1201–1204

  24. Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J., Van den Broeck, W.: What’s in a crowd? Analysis of face-to-face behavioral networks. J. Theor. Biol. 271(1), 166–180 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  25. KONECT: Hamsterster friendships network dataset, April 2017 (2017)

  26. Lancichinetti, A., Fortunato, S.: Community detection algorithms: a comparative analysis. Phys. Rev. E 80(5), 56117 (2009)

    Article  Google Scholar 

  27. Lindner, G., Staudt, C.L., Hamann, M., Meyerhenke, H., Wagner, D.: Structure-preserving sparsification of social networks. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 448–454. ACM, Paris, France (2015)

  28. Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), pp. 93–106. ACM Press, New York (2008)

  29. Macwan, K.R., Patel, S.J.: \(k\)-NMF anonymization in social network data publishing. Comput. J. 61(4), 601–613 (2018)

    Google Scholar 

  30. Ma, T., Zhang, Y., Cao, J., Shen, J., Tang, M., Tian, Y., Al-Dhelaan, A., Al-Rodhaan, M.: KDVEM: a \(k\)-degree anonymity with vertex and edge modification algorithm. Computing 97(12), 1165–1184 (2015)

    MathSciNet  MATH  Google Scholar 

  31. Nagle, F.: Privacy breach analysis in social networks. Mining Social Networks and Security Informatics, pp. 63–77. Springer, Dordrecht (2013)

    Chapter  Google Scholar 

  32. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. In: Proceedings of the 7th International World Wide Web Conference (WWW), pp. 161–172. Brisbane, Australia (1998)

  33. Pons, P., Latapy, M.: Computing communities in large networks using random walks. Computer and Information Sciences (ISCIS), vol. 10, pp. 284–293. Springer, Berlin (2005)

    Google Scholar 

  34. Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)

    Article  Google Scholar 

  35. Sala, A., Zhao, X., Wilson, C., Zheng, H., Zhao, B.Y.: Sharing graphs using differentially private graph models. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference (IMC ’11), Berlin, Germany, pp. 81–98 (2011)

  36. Sarma, A.D., Molla, A.R., Pandurangan, G., Upfal, E.: Fast distributed PageRank computation. Theor. Comput. Sci. 561(B), 113–121 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  37. Sweeney, L.: \(k\)-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002)

    MathSciNet  MATH  Google Scholar 

  38. Wagner, I., Eckhoff, D.: Technical privacy metrics. ACM Comput. Surv. 51(3), 1–38 (2018)

    Article  Google Scholar 

  39. Wang, Y., Xie, L., Zheng, B., Lee, K.C.K.: High utility K-anonymization for social network publishing. Knowl. Inf. Syst. (KAIS) 41(3), 697–725 (2014)

    Article  Google Scholar 

  40. Wang, Y., Zheng, B.: Preserving privacy in social networks against connection fingerprint attacks. In: International Conference on Data Engineering (ICDE), pp. 54–65. IEEE, Seoul, South Korea (2015)

  41. Yang, Y., Lutes, J., Li, F., Luo, B., Liu, P.: Stalking online: on user privacy in social networks. In: Proceedings of 2nd ACM Conference on Data and Application Security and Privacy (CODASPY’12), pp. 37–48 (2012)

  42. Ying, X ., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp. 739–750. SIAM, Atlanta (2008)

  43. Ying, X., Pan, K., Wu, X., Guo, L.: Comparisons of randomization and \(k\)-degree anonymization schemes for privacy preserving social network publishing. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis (SNA-KDD), pp. 10:1-10:10. ACM Press, New York (2009)

  44. Yuan, M., Chen, L., Yu, P.S., Yu, T.: Protecting sensitive labels in social network data anonymization. IEEE Trans. Knowl. Data Eng. 25(3), 633–647 (2013)

    Article  Google Scholar 

  45. Zhang, K., Lo, D., Lim, E.-P., Prasetyo, P.K.: Mining indirect antagonistic communities from social interactions. Knowl. Inf. Syst. 35(3), 553–583 (2013)

    Article  Google Scholar 

  46. Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: Proceedings of the 24th International Conference on Data Engineering (ICDE), pp. 506–515. IEEE Computer Society, Washington (2008)

  47. Zou, L., Chen, L., Özsu, M.T.: \(K\)-Automorphism: a general framework for privacy preserving network publication. Proc. VLDB Endow. 2(1), 946–957 (2009)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Spanish Government, in part under Grant RTI2018-095094-B-C22 “CONSENT” and in part under Grant TIN2014-57364-C2-2-R “SMARTGLACIS”.

Author information

Authors and Affiliations

  1. Center for Cybersecurity Research of Catalonia (CYBERCAT), Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Barcelona, Spain

    Jordi Casas-Roma

Authors
  1. Jordi Casas-Roma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jordi Casas-Roma.

Ethics declarations

Conflict of interest

Author Jordi Casas-Roma declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Casas-Roma, J. DUEF-GA: data utility and privacy evaluation framework for graph anonymization. Int. J. Inf. Secur. 19, 465–478 (2020). https://doi.org/10.1007/s10207-019-00469-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10207-019-00469-4

Keywords

Search

Navigation