User survey regarding the needs of network researchers in trace-anonymization tools

To understand the needs of network researchers in an anonymization tool, we conducted a survey on the network researchers. We invited network researchers world-wide to the survey by sending invitation emails to well-known mailing lists whose subscribers may be interested in network research with collecting, sharing and sanitizing network traces

Capacity and Variability Analysis of the IEEE 802.11 MAC Protocol

{\em Packet error} in the IEEE 802.11 network %which is due to non-ideal channel condition and wireless device variability, is one source of performance degradation and its variability. %Therefore the effect of packet errors, along with {\em collision %avoidance} and {\em hidden terminals}, is among the most important %considerations in performance anaylsis of the 802.11 MAC protocol. Most of the previous works study how {\em collision avoidance} and {\em hidden terminals} affect 802.11 performance metrics, such as probability of a collision and saturation throughput. In this paper we focus on the effect of packet errors on capacity and variability of the 802.11 MAC protocol. We develope a new analytical model, called $p_e$-Model, by extending the existing model (Tay and Chua's model) to incorporate {\em packet error probability} $p_e$. With $p_e$-Model, we successfully analyze capacity and variability of the 802.11 MAC protocol. The variability analysis shows that increasing packet error probability by $\Delta p_e$ has more effect on saturation throughput, than adding $0.5 W \Delta p_e$ stations, where $W$ is the minimum contention window size, We also show the numerical validation of $p_e$-Model with 802.11 MAC-level simulator. (UMIACS-TR-2003-45

A Correlation Attack Against User Mobility Privacy in a Large-scale WLAN network

User association logs collected from real-world wireless LANs have facilitated wireless network research greatly. To protect user privacy, the common practice in sanitizing these data before releasing them to the public is to anonymize users\u27 sensitive information such as the MAC addresses of their devices and their exact association locations. In this work,we demonstrate that these sanitization measures are insufficient in protecting user privacy from a novel type of correlation attack that is based on CRF (Conditional Random Field). In such a correlation attack, the adversary observes the victim\u27s AP (Access Point) association activities for a short period of time and then infers her corresponding identity in a released user association dataset. Using a user association log that contains more than three thousand users and millions of AP association records, we demonstrate that the CRF-based technique is able to pinpoint the victim\u27s identity exactly with a probability as high as 70%

Catch, Clean, and Release: A Survey of Obstacles and Opportunities for Network Trace Sanitization

Network researchers benefit tremendously from access to traces of production networks, and several repositories of such network traces exist. By their very nature, these traces capture sensitive business and personal activity. Furthermore, network traces contain significant operational information about the target network, such as its structure, identity of the network provider, or addresses of important servers. To protect private or proprietary information, researchers must “sanitize” a trace before sharing it. \par In this chapter, we survey the growing body of research that addresses the risks, methods, and evaluation of network trace sanitization. Research on the risks of network trace sanitization attempts to extract information from published network traces, while research on sanitization methods investigates approaches that may protect against such attacks. Although researchers have recently proposed both quantitative and qualitative methods to evaluate the effectiveness of sanitization methods, such work has several shortcomings, some of which we highlight in a discussion of open problems. Sanitizing a network trace, however challenging, remains an important method for advancing network–based research

WLAN Workload Characterization

In this dissertation, we address the problem of workload characterization in a wireless LAN (WLAN). Workload is generated by applications and users trying to carry out some of their functions. We attempt to capture such application- and user-level characteristics from the information gathered at the MAC level. Developing an understandable description of the workload requires making some abstractions at the application- and user-level. Our approach is to consider the workload in terms of ``sessions", where a session is an application- and user-level sequence of exchanges. We attempt to capture the session by considering an inactive duration in the activities between a wireless end-point and the network. We consider workload to consist of a population of sessions for which a probability distribution function can be defined. Considering this distribution function to be a mixture distribution, we attempt to find the components by using non-parametric clustering technique. As the number of types of user level activities is not likely to be very large, we expect that we can associate a distinct activity with each such component. In this work, we identify such components and analyze the traffic and protocol characteristics of each component. Moreover, we empirically show that the identified workload components can effectively represent the actual WLAN workload and its daily variations