Models of Privacy Preserving Traffic Tunneling

Abstract

Encrypted protocols, such as Secure Socket Layer (SSL), are becoming more prevalent because of the growing use of e-commerce, anonymity services, and secure authentication. Likewise, traffic analysis is becoming more common because it is often the only way to analyze these protocols. Although there are many valid uses for traffic analysis (such as network policy enforcement and intrusion detection), it can also be used to maliciously compromise the secrecy or privacy of a user. While the payload can be strongly protected by encryption, analysis of traffic patterns can yield information about the type and nature of traffic. In this paper we use simulation and analytic models to examine the impact on user experience of a scheme that masks the behavior of real traffic by embedding it in synthetic, encrypted, cover traffic. Through simulation and an analytic model we investigate the effects on the user experience using disparate and similar traffic models. This point provides a novel context where we observe the synergy of simulation and analytic modeling. We show that a detailed simulation model of network traffic characteristics can be used to estimate the parameters of an analytic model of tunneling. We see that the accuracy of the model’s predictions is directly dependent on the accuracy of parameters we obtain from the simulation. However, the simulation model does not need to have any concept of tunneling. Using simulation and analytic modeling together, we obtain an analysis whose whole is greater than the sum of its parts.