Skip to main content
SpringerLink
Log in

A probabilistic model for anonymity analysis of anonymous communication networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

An anonymous communication network (ACN) is supposed to provide its users with anonymity attributes. As a practical matter, we need to have a means to predict the level of the anonymity provided by ACN. In this paper, we propose a probabilistic model for the security analysis of ACNs, thereby quantifying their loss of anonymity. To be precise, we have tried to obtain the probability distribution of potential senders of a message sent from an unknown source to a specific destination. With the probability distribution in hand, it is possible to define and derive some anonymity metrics. The evaluated metrics help us to gain an understanding of how much such a network may be vulnerable to attacks aiming at revealing the identity of senders of messages. Consequently, new rerouting policies and strategies can be utilized to increase the anonymity level of the network. The quantitative analysis is performed from a general perspective and the applicability of the model is not limited to a specific network. The evaluation process of the metrics using the proposed model is clarified by an illustrative example.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Nambiar, A., & Wright, M. (2006). Salsa: A structured approach to large-scale anonymity. In Proceedings of the 13th ACM conference on computer and communications security (pp. 17–26).

  2. Freedman, M. J., & Morris, R. (2002). Tarzan: A peer-to-peer anonymizing network layer. In Proceedings of the ACM conference on computer and communications Security (pp. 193–206). New York, NY.

  3. Dingledine, R., Mathewson, N., & Syverson, P. (2004). Tor: The second generation onion router. In Proceedings of the USENIX security symposium (Vol. 21, p. 21).

  4. Mittal, P., & Borisov, N. (2012). Information leaks in structured peer-to-peer anonymous communication systems. ACM Transactions on Information and System Security (TISSEC), Special Issue on Computer and Communications Security, 15(1), 1–12.

    Article  Google Scholar 

  5. Hopper, N., Vasserman, E. Y., & Chan-Tin, E. (2010). How much anonymity does network latency leak? ACM Transactions on Information and System Security (TISSEC), 13(2), 1–28.

    Article  Google Scholar 

  6. Reiter, M., & Rubin, A. (1998). Crowds: Anonymity for Web transactions. ACM Transactions on Information and System Security (TISSEC), 1(1), 66–92.

    Article  Google Scholar 

  7. Akhoondi, M., Yu, C., & Madhyastha, H. V. (2012). LASTor: A low-latency AS-aware Tor client. In Proceedings of the 2012 IEEE symposium on security and privacy (pp. 476–490).

  8. Al Sabah, M., Bauer, K., & Goldberg, I. (2012). Enhancing Tor’s performance using real-time traffic classification. In Proceedings of the 19th ACM conference on computer and communications security (CCS’12) (pp. 73–84).

  9. Diaz, C., Seys, S., Claessens, J., & Preneel, B. (2002). Towards measuring anonymity. In Designing privacy enhancing technologies (PET’02), Lecture Notes in Computer Science (Vol. 2482, pp. 54–68).

  10. Gierlichs, B., Troncoso, C., Diaz, C., Preneel, B., & Verbauwhede, I. (2008). Revisiting a combinatorial approach toward measuring anonymity. In Proceedings of the workshop on privacy in the electronic society (WPES’08) (pp. 111–116).

  11. Edman, M., Sivrikaya, F., & Yener, B. (2007). A combinatorial approach to measuring anonymity. In Proceedings of the 2007 IEEE intelligence and security informatics (pp. 356–363).

  12. Chen, H., & Malacaria, P. (2009). Quantifying maximal loss of anonymity in protocols. In Proceedings of the 2009 ACM symposium on information, computer and communications security (ASIACCS’09) (pp. 10–12).

  13. Chatzikokolakis, K., Palamidessi, C., & Panangaden, P. (2008). Anonymity protocols as noisy channels. Information and Computation, 206, 378–401.

    Article  Google Scholar 

  14. Deng, Y., Pang, J., & Wu, P. (2007). Measuring anonymity with relative entropy. In Proceedings of the 4th workshop on formal aspects in security and trust (FAST’06), Lecture Notes in Computer Science (Vol. 4691, pp. 65–79).

  15. Huang, D. (2009). On an information theoretic approach to model anonymous MANET communications. In Proceedings of the IEEE international symposium on information theory (ISIT’09) (pp. 1629–1633).

  16. Zhu, Y., & Bettati, R. (2009). Information leakage as a model for quality of anonymity networks. IEEE Transactions on Parallel and Distributed Systems, 20(4), 540–552.

    Article  Google Scholar 

  17. Venkitasubramaniam, P., & Tong, L. (2012). A game theoretic approach to anonymous networking. ACM/IEEE Transaction on Networking, 20(3), 892–905.

    Article  Google Scholar 

  18. Guan, Y., Fu, X., Bettati, R., & Zhao, W. (2004). A quantitative analysis of anonymous communications. IEEE Transactions on Reliability, 53(1), 103–116.

    Article  Google Scholar 

  19. Troncoso, C., & Danezis, G. (2009). The Bayesian traffic analysis of mix networks. In Proceedings of the 2009 ACM conference on computer and communications security (CCS’09) (pp. 369–379). Chicago, IL.

  20. Vuković, O., Dán, G., & Karlsson, G. (2013). Traffic analysis attacks in anonymity networks: Relationship anonymity—overhead trade-off. Technical Report, KTH, TRITA-EE, 2013, 007.

    Google Scholar 

  21. Feigenbaum, J., Johnson, A., & Syverson, P. (2012). A probabilistic analysis of onion routing in a black-box model. ACM Transactions on Information and System Security (TISSEC), 15(3), 14:1–14:28.

    Article  Google Scholar 

  22. Loesing, K., Murdoch, S., & Dingledine, R. (2010). A case study on measuring statistical data in the Tor anonymity network. In Proceedings of the 14th international conference on financial cryptography and data security (FC’10) (pp. 203-215).

  23. Shmatikov, V. (2004). Probabilistic analysis of an anonymity system. Journal of Computer Security, 12(3–4), 355–377.

    Article  Google Scholar 

  24. Trivedi, K. S. (2001). Probability and statistics with reliability, queuing, and computer science applications (2nd ed.). Hoboken, NJ: Wiley.

    Google Scholar 

  25. Cover, T. M., & Thomas, J. A. (2006). Elements of information theory (2nd ed.). Hoboken, NJ: Wiley.

    Google Scholar 

  26. Danezis, G., Diaz, C., Käsper, E., & Troncoso, C. (2009). The wisdom of crowds: Attacks and optimal constructions. In Proceedings of the 14th European symposium on research in computer security (ESORICS’09), Saint Malo, France, September 21–25, Lecture Notes in Computer Science (Vol. 5789, pp. 406–423). Springer.

  27. Diaz, C., Murdoch, S. J., & Troncoso, C. (2010). Impact of network topology on anonymity and overhead in low-latency anonymity networks. In Proceedings of the 10th privacy enhancing technologies symposium (PETS’10), Lecture Notes in Computer Science (Vol. 6205, pp. 184–201).

  28. Bauer, K., Juen, J., Borisov, N., Grunwald, D., Sicker, D., & McCoy, D. (2010). On the optimal path length for Tor. In Proceedings of the 3rd hot topics in privacy enhancing technologies (HotPETS’10).

  29. Levitin, A. V. (2012). Introduction to the design and analysis of algorithms (3rd ed.). Pearson.

  30. Martínez, A. R. (2012). A survey on solutions and main free tools for privacy enhancing Web communications. Journal of Network and Computer Applications, 35(5), 1473–1492.

    Article  Google Scholar 

  31. Shirazi, F., Diaz, C., Mullan, C., Wright, J., & Buchmann, J. (2013). Towards measuring resilience in anonymous communication networks. In Proceedings of the 6th hot topics in privacy enhancing technologies (HotPETs’13), Bloomington.

  32. Hosp, B., & Vora, P. L. (2008). An information-theoretic model of voting systems. Mathematical and Computer Modelling, 48(9–10), 1628–1645.

    Article  Google Scholar 

  33. Shokri, R., Theodorakopoulos, G., Boudec, J., & Hubaux, J. (2011). Quantifying location privacy. In Proceedings of the IEEE symposium on security and privacy (S&P’11) (pp. 247–262). Washington, DC.

  34. Almasizadeh, J., & Abdollahi Azgomi, M. (2013). A stochastic model of attack process for the evaluation of security metrics. Computer Networks, 57(10), 2158–2179.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the Research Institute for ICT of Iran for their financial support of this research. We are also grateful to the guest editors and anonymous referees of this Special Issue of Telecommunication Systems journal, whose comments substantially improved this paper.

Author information

Authors and Affiliations

  1. School of Computer Engineering, Iran University of Science and Technology, Hengam St., Resalat Sq., Tehran, Postal Code: 16846-13114, Iran

    Jaafar Almasizadeh & Mohammad Abdollahi Azgomi

Authors
  1. Jaafar Almasizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Abdollahi Azgomi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Abdollahi Azgomi.

Additional information

This research was supported by the Research Institute for ICT of Iran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Almasizadeh, J., Abdollahi Azgomi, M. A probabilistic model for anonymity analysis of anonymous communication networks. Telecommun Syst 69, 171–186 (2018). https://doi.org/10.1007/s11235-018-0454-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-018-0454-0

Keywords

Search

Navigation