Abstract
With the increasing requirement of privacy protection, various anonymity communication systems are designed and implemented. However, in the current communication infrastructure, traffic data can be gathered at moderate cost by adversary. Based on the traffic data, they can easily correlate the input links with output links by applying powerful traffic analysis techniques. In this paper, a Hidden Markov Model (HMM) approach is proposed to analyze one of the important anonymity systems, continuous mixes, which individually delays messages instead of processing batch messages. This approach consists of two parts, arrival traffic model and departure traffic model based on HMM, which capture the mean rates of the arrival and departure messages respectively. By using this approach to analyze anonymity of continuous mixes, a successful anonymity analysis can not be guaranteed, especially while the arrival traffic rate is greater than the departure traffic rate. In order to achieve better anonymity results, a new countermeasure is proposed, which inserts a minimum number of dummy traffic flows to ensure better anonymity of continuous mixes and protects users against various traffic analyses.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chaum, D.L.: Untraceable electronic mail, return addresses, and digital pseudonyms. Communications of the ACM 24(2), 84–88 (1981)
DÃaz, C., Serjantov, A.: Generalising mixes. In: Dingledine, R. (ed.) PET 2003. LNCS, vol. 2760. Springer, Heidelberg (2003)
Serjantov, A., Dingledine, R., Syverson, P.F.: From a Trickle to a Flood: Active Attacks on Several Mix Types. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 36–52. Springer, Heidelberg (2003)
Kesdogan, D., Egner, J., Büschkes, R.: Stop-and-Go-MIXes Providing Probabilistic Anonymity in an Open System. In: Aucsmith, D. (ed.) IH 1998. LNCS, vol. 1525, Springer, Heidelberg (1998)
Levine, B.N., Reiter, M.K., Wang, C., et al.: Timing Attacks in Low-Latency Mix Systems. In: Juels, A. (ed.) FC 2004. LNCS, vol. 3110. Springer, Heidelberg (2004)
Shmatikov, V., Wang, M.H.: Timing Analysis in Low-Latency Mix Networks: Attacks and Defenses. In: Gollmann, D., Meier, J., Sabelfeld, A. (eds.) ESORICS 2006. LNCS, vol. 4189, pp. 18–33. Springer, Heidelberg (2006)
Agrawal, D., Kesdogan, D., Penz, S.: Probabilistic treatment of MIXes to hamper traffic analysis. In: Proceedings of Symposium on Security and Privacy 2003, pp. 16–27 (2003)
Danezis, G., Diaz, C., Troncoso, C.: Two-sided Statistical Disclosure Attack. Privacy Enhancing Technologies, 30–44 (2007)
Yu, W., Fu, X., Graham, S., et al.: DSSS-Based Flow Marking Technique for Invisible Traceback. In: Proceedings of the 2007 IEEE Symposium on Security and Privacy, pp. 18–32 (2007)
Danezis, G.: The Traffic Analysis of Continuous-Time Mixes. Privacy Enhancing Technologies, 35–50 (2005)
DÃaz, C., Sassaman, L., Dewitte, E.: Comparison Between Two Practical Mix Designs. In: Samarati, P., Ryan, P.Y.A., Gollmann, D., Molva, R. (eds.) ESORICS 2004. LNCS, vol. 3193, pp. 141–159. Springer, Heidelberg (2004)
Serjantov, A.: A Fresh Look at the Generalised Mix Framework. In: Borisov, N., Golle, P. (eds.) PET 2007. LNCS, vol. 4776, p. 17. Springer, Heidelberg (2007)
Serjantov, A., Danezis, G.: Towards an Information Theoretic Metric for Anonymity. Privacy Enhancing Technologies, 259–263 (2003)
Salvo Rossi, P., Romano, G., Palmieri, F., Iannello, G.: Joint end-to-end loss-delay hidden Markov model for periodic UDP traffic over the Internet. IEEE Transactions on Signal Processing 54(2), 530–541 (2006)
Muscariello, L., Mellia, M., Meo, M., et al.: Markov models of internet traffic and a new hierarchical MMPP model. Computer Communications 28(16), 1835–1851 (2005)
Bilmes, J.A.: A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. International Computer Science Institute 4 (1998)
Liporace, L.: Maximum likelihood estimation for multivariate observations of Markov sources. IEEE Transactions on Information Theory 28(5), 729–734 (1982)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications inspeech recognition[J]. Proceedings of the IEEE 77(2), 257–286 (1989)
Leroux, B.G., Puterman, M.L.: Maximum-penalized-likelihood estimation for independent and Markov-dependent mixture models. Biometrics 48(2), 545–558 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ling, Z., Luo, J., Yang, M. (2008). An HMM Approach to Anonymity Analysis of Continuous Mixes. In: Ishikawa, Y., et al. Advanced Web and Network Technologies, and Applications. APWeb 2008. Lecture Notes in Computer Science, vol 4977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89376-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-89376-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89375-2
Online ISBN: 978-3-540-89376-9
eBook Packages: Computer ScienceComputer Science (R0)