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A Literature Survey and Classifications on Data Deanonymisation

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Risks and Security of Internet and Systems (CRiSIS 2015)

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Abstract

The problem of disclosing private anonymous data has become increasingly serious particularly with the possibility of carrying out deanonymisation attacks on publishing data. The related work available in the literature is inadequate in terms of the number of techniques analysed, and is limited to certain contexts such as Online Social Networks. We survey a large number of state-of-the-art techniques of deanonymisation achieved in various methods and on different types of data. Our aim is to build a comprehensive understanding about the problem. For this survey, we propose a framework to guide a thorough analysis and classifications. We are interested in classifying deanonymisation approaches based on type and source of auxiliary information and on the structure of target datasets. Moreover, potential attacks, threats and some suggested assistive techniques are identified. This can inform the research in gaining an understanding of the deanonymisation problem and assist in the advancement of privacy protection.

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References

  1. Ding, X., Zhang, L., Wan, Z., Gu, M.: A brief survey on de-anonymization attacks. In: Online Social Networks, in International Conference on Computational Aspects of Social Networks, pp. 611–615 (2010)

    Google Scholar 

  2. El Emam, K., Jonker, E., Arbuckle, L., Malin, B.: A systematic review of re-identification attacks on health data. PLoS ONE 6(12), e28071 (2011)

    Article  Google Scholar 

  3. Sharma, S., Gupta, P., Bhatnagar, V.: Anonymisation in social network: a literature survey and classification. Int. J. Soc. Netw. 1(1), 51–66 (2012)

    Google Scholar 

  4. Toch, E., Wang, Y., Cranor, L.F.: Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems. User Model. User-adapt. Interact. 22(1–2), 203–220 (2012)

    Article  Google Scholar 

  5. Ohm, P.: Broken promises of privacy: responding to the surprising failure of anonymization. UCLA Law Rev. 57, 1701 (2010)

    Google Scholar 

  6. Alexin, Z.: Does fair anonymization exist? Int. Rev. Law, Comput. Technol. 28(1), 21–44 (2014)

    Article  Google Scholar 

  7. Dwork, C., Naor, M.: On the difficulties of disclosure prevention in statistical databases or the case for differential privacy. J. Priv. Confidentiality 2(1), 93–107 (2008)

    Google Scholar 

  8. O’Hara, K.: Transparent Government, Not Transparent Citizens: A Report on Privacy and Transparency for the Cabinet Office (2011)

    Google Scholar 

  9. Sun, X., Wang, H., Zhang, Y.: On the identity anonymization of high-dimensional rating data, No. March (2011), pp. 1108–1122 (2012)

    Google Scholar 

  10. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty, Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Bayardo, R.J., Agrawal, R.: Data privacy through optimal k-anonymization. In: 21st International Conference Data and Engineering, pp. 217–228 (2005)

    Google Scholar 

  12. Li, N.: Provably Private Data Anonymization: Or, k-Anonymity Meets Differential Privacy (2010)

    Google Scholar 

  13. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1(1), 3–es (2007)

    Article  Google Scholar 

  14. Zhou, B., Pei, J.: The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Know. Inf. Syst. 28(1), 47–77 (2010)

    Article  Google Scholar 

  15. Li, N.: t-closeness: privacy beyond k-anonymity and -diversity. ICDE 7, 106–115 (2007)

    Google Scholar 

  16. Domingo-Ferrer, J., Torra, V.: A critique of k-anonymity and some of its enhancements. In: Third International Conference Availability, Reliability and Security, pp. 990–993 (2008)

    Google Scholar 

  17. Narayanan, A., Shi, E., Rubinstein, B.I.P.: Link prediction by de-anonymization: how we won the Kaggle social network challenge. In: Neural Networks (IJCNN) (2011)

    Google Scholar 

  18. Sharad, K., Danezis, G.: De-anonymizing D4D datasets. In: Workshop on Hot Topics in Privacy Enhancing Technologies (2013)

    Google Scholar 

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

    Google Scholar 

  20. Bender, S., Brand, R., Bacher, J.: Re-identifying register data by survey data: an empirical study. Stat. J. United Nations ECE 18(00311), 373–381 (2001)

    MATH  Google Scholar 

  21. Gulyás, G., Imre, S.: Analysis of identity separation against a passive clique-based de-anonymization attack. Infocomm. J. 3(4), 1–10 (2011)

    Google Scholar 

  22. Torra, V., Stokes, K.: A formalization of re-identification in terms of compatible probabilities. CoRR, abs/1301.5, pp. 1–20 (2013)

    Google Scholar 

  23. Datta, A., Sharma, D., Sinha, A.: Provable de-anonymization of large datasets with sparse dimensions. in principles of security and trust (2012)

    Google Scholar 

  24. Gulyas, G.G., Imre, S.: Measuring importance of seeding for structural de-anonymization attacks in social networks. In: The Sixth IEEE Workshop on SECurity and SOCial Networking, pp. 610–615 (2014)

    Google Scholar 

  25. Hay, M., Miklau, G., Jensen, D.: Resisting structural re-identification in anonymized social networks. Proceedings of the VLDB Endowment 1(1), 102–114 (2008)

    Article  Google Scholar 

  26. Dankar, F.K., El Emam, K., Neisa, A., Roffey, T.: Estimating the re-identification risk of clinical data sets. BMC Med. Inf. Decis. Making 12(1), 66 (2012)

    Article  Google Scholar 

  27. Cecaj, A., Mamei, M., Bicocchi, N.: Re-identification of anonymized CDR datasets using social network data. In: The Third IEEE International Workshop on the Impact of Human Mobility in Pervasive Systems and Applications, pp. 237–242 (2014)

    Google Scholar 

  28. Zhang, A., Xie, X., Chang, K.C.-C., Gunter, C.A., Han, J., Wang, X.F.: Privacy risk in anonymized heterogeneous information networks. In: EDBT (2014)

    Google Scholar 

  29. Pedarsani, P., Grossglauser, M.: On the privacy of anonymized networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2011, p. 1235 (2011)

    Google Scholar 

  30. Zhu, T., Wang, S., Li, X., Zhou, Z., Zhang, R.: Structural attack to anonymous graph of social networks. Math. Probl. Eng. 2013, 1–8 (2013)

    MATH  Google Scholar 

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

    Google Scholar 

  32. Srivatsa, M., Hicks, M.: Deanonymizing mobility traces: using social networks as a side-channel. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security. ACM (2012)

    Google Scholar 

  33. Sharad, K., Danezis, G.: An automated social graph de-anonymization technique. arXiv Prepr, arXiv:1408.1276 (2014)

  34. Nilizadeh, S., Kapadia, A., Ahn, Y.-Y.: Community-enhanced de-anonymization of online social networks. In: CCS 2014 (2014)

    Google Scholar 

  35. Peng, W., Li, F., Zou, X., Wu, J.: A two-stage deanonymization attack against anonymized social networks. IEEE Trans. Comput. 63(2), 290–303 (2014)

    Article  MathSciNet  Google Scholar 

  36. Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou R3579X? anonymized social networks, hidden patterns, and structural steganography. In: Proceedings of the 16th International Conference on World Wide Web. ACM (2007)

    Google Scholar 

  37. Bringmann, K., Friedrich, T., Krohmer, A.: De-anonymization of heterogeneous random graphs in quasilinear time. In: ESA, pp. 197–208 (2014)

    Google Scholar 

  38. Simon, B., Gulyás, G.G., Imre, S.: Analysis of grasshopper, a novel social network de-anonymization algorithm. Periodica Polytechnica Electr. Eng. Comput. Sci. 58(4), 161–173 (2014)

    Article  Google Scholar 

  39. Kazemi, E., Hassani, S.H., Grossglauser, M.: Growing a graph matching from a handful of seeds. In: 41st International Conference on Very Large Data Bases (2015)

    Google Scholar 

  40. Ding, X., Zhang, L., Wan, Z., Gu, M.: De-anonymizing dynamic social networks. In: IEEE Global Telecommunications Conference – GLOBECOM, pp. 1–6 (2011)

    Google Scholar 

  41. Gambs, S., Killijian, M.-O., Núñez del Prado Cortez, M.: De-anonymization attack on geolocated data. J. Comput. Syst. Sci. 80(8), 1597–1614 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  42. Ji, S., Li, W., Srivatsa, M., He, J.S., Beyah, R.: Structure based data de-anonymization of social networks and mobility traces (2014)

    Google Scholar 

  43. Okuno, T., Ichino, M., Kuboyama, T., Yoshiura, H.: Content-based de-anonymisation of tweets. In: The Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 53–56 (2011)

    Google Scholar 

  44. Fu, H., Zhang, A., Xie, X.: Effective social graph de-anonymization based on graph structure and descriptive information. ACM Trans. Intell. Syst. Technol. 6(4), 1–29 (2008)

    Article  Google Scholar 

  45. Unnikrishnan, J., Naini, F. M.: De-anonymizing private data by matching statistics. In: Allerton Conference on Communication, Control, and Computing, No. EPFL-CONF-196580 (2013)

    Google Scholar 

  46. Zheleva, E., Getoor, L.: To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles, pp. 531–540 (2009)

    Google Scholar 

  47. Wondracek, G., Holz, T., Kirda, E., Kruegel, C.: A practical attack to de-anonymize social network users. In: IEEE Symposium on Security and Privacy, pp. 223–238 (2010)

    Google Scholar 

  48. Korayem, M., Crandall, D. J.: De-anonymizing users across heterogeneous social computing platforms. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, pp. 1–4 (2013)

    Google Scholar 

  49. Lane, N.D., Xie, J., Moscibroda, T., Zhao, F.: On the feasibility of user de-anonymization from shared mobile sensor data. In: Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones - PhoneSense 2012, pp. 1–5 (2012)

    Google Scholar 

  50. Merener, M.M.: Theoretical results on de-anonymization via linkage attacks. Trans. Data Priv. 5(2), 377–402 (2012)

    MathSciNet  Google Scholar 

  51. Frankowski, D., Cosley, D., Sen, S., Terveen, L., Riedl, J.: You are what you say: privacy risks of public mentions. In: Proceedings of the 29th SIGIR 2006, pp. 565–572 (2006)

    Google Scholar 

  52. Malin, B., Sweeney, L.: How (not) to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems. J. Biomed. Inform. 37(3), 179–192 (2004)

    Article  Google Scholar 

  53. Malin, B., Sweeney, L., Newton, E.: Trail re-identification: learning who you are from where you have been. In: Workshop on Privacy in Data (2003)

    Google Scholar 

  54. Foukarakis, M., Antoniades, D., Antonatos, S., Markatos, E.P.: On the anonymization and deanonymization of netflow traffic. In: Proceedings of FloCon (2008)

    Google Scholar 

  55. Biryukov, A., Pustogarov, I., Weinmann, R.-P.: Trawling for tor hidden services: detection, measurement, deanonymization. In: 2013 IEEE Symposium on Security and Privacy, pp. 80–94 (2013)

    Google Scholar 

  56. Pataky, M.: De-anonymization of an Internet user based on his web browser. In: CER Comparative European Research, pp. 125–128 (2014)

    Google Scholar 

  57. Danezis, G., Troncoso, C.: You cannot hide for long: de-anonymization of real-world dynamic behaviour. In: WPES 2013, pp. 49–59 (2013)

    Google Scholar 

  58. Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., Shmatikov, V.: You might also like: privacy risks of collaborative filtering, privacy risks of collaborative filtering. In: IEEE Symposium on Security and Privacy. IEEE (2011)

    Google Scholar 

  59. Danezis, G., Troncoso, C.: Vida: how to use Bayesian inference to de-anonymize persistent communications. In: Privacy Enhancing Technologies (2009)

    Google Scholar 

  60. Ji, S., Li, W., Srivatsa, M., Beyah, R.: Structural data de-anonymization: quantification, practice, and implications. In: CCS 2014 (2014)

    Google Scholar 

  61. Ji, S., Li, W., Gong, N.Z., Mittal, P., Beyah, R.: On your social network de-anonymizablity: quantification and large scale evaluation with seed knowledge. In: The 2015 Network and Distributed System Security (NDSS) Symposium, San Diego, CA, US, pp. 8–11 (2015)

    Google Scholar 

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Acknowledgments

This research is funded by University of Tabuk in Saudi Arabia and supported by Saudi Arabian Cultural Bureau in London.

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Correspondence to Dalal Al-Azizy .

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Al-Azizy, D., Millard, D., Symeonidis, I., O’Hara, K., Shadbolt, N. (2016). A Literature Survey and Classifications on Data Deanonymisation. In: Lambrinoudakis, C., Gabillon, A. (eds) Risks and Security of Internet and Systems. CRiSIS 2015. Lecture Notes in Computer Science(), vol 9572. Springer, Cham. https://doi.org/10.1007/978-3-319-31811-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-31811-0_3

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