Large-scale Wireless Local-area Network Measurement and Privacy Analysis

The edge of the Internet is increasingly becoming wireless. Understanding the wireless edge is therefore important for understanding the performance and security aspects of the Internet experience. This need is especially necessary for enterprise-wide wireless local-area networks (WLANs) as organizations increasingly depend on WLANs for mission- critical tasks. To study a live production WLAN, especially a large-scale network, is a difficult undertaking. Two fundamental difficulties involved are (1) building a scalable network measurement infrastructure to collect traces from a large-scale production WLAN, and (2) preserving user privacy while sharing these collected traces to the network research community. In this dissertation, we present our experience in designing and implementing one of the largest distributed WLAN measurement systems in the United States, the Dartmouth Internet Security Testbed (DIST), with a particular focus on our solutions to the challenges of efficiency, scalability, and security. We also present an extensive evaluation of the DIST system. To understand the severity of some potential trace-sharing risks for an enterprise-wide large-scale wireless network, we conduct privacy analysis on one kind of wireless network traces, a user-association log, collected from a large-scale WLAN. We introduce a machine-learning based approach that can extract and quantify sensitive information from a user-association log, even though it is sanitized. Finally, we present a case study that evaluates the tradeoff between utility and privacy on WLAN trace sanitization

Human Behavior and Challenges of Anonymizing WLAN Traces

Abstract—With the wide spread deployment of wireless LANs (WLANs), it is becoming necessary to conduct analysis of libraries of measurements taken from such operational networks. The avail-ability of these trace libraries can be quite useful to provide realistic models of network load and user mobility, among others. To maintain user privacy, techniques of trace anonymization may be used to hide information. In this paper, we propose to study the fundamental trade off between the utility of WLAN traces and its privacy. The study provides several realistic case studies in which privacy attacks may be conducted, and questions the efficacy of existing anonymization techniques. Our initial quantitative analysis to esti-mate mobile users ’ k-anonymity in WLAN traces shows surprisingly unique usage patterns, which may compromise anonymity. The main contribution of this paper is to articulate the compelling challenges facing anonymization of wireless networks traces and to shed some light on the answer to a most intriguing question: Just how private are wireless networks traces? I