Li, Shuai2019-08-202019-08-202019-06https://hdl.handle.net/11299/206233University of Minnesota Ph.D. dissertation. June 2019. Major: Computer Science. Advisor: Nick Hopper. 1 computer file (PDF); viii, 76 pages.The pervasive Internet surveillance and the wide-deployment of Internet censors lead to the need for making traffic anonymous. However, recent studies demonstrate the information leakage in anonymous traffic that can be used to de-anonymize Internet users. This thesis focuses on how to measure and prevent such information leakage in anonymous traffic. Choosing Tor anonymous networks as the target, the first part of this thesis conducts the first large-scale information leakage measurement in anonymous traffic and discovers that the popular practice of validating WF defenses by accuracy alone is flawed. We make this measurement possible by designing and implementing our website fingerprint density estimation (WeFDE) framework. The second part of this thesis focuses on preventing such information leakage. Specifically, we design two anti-censorship systems which are able to survive traffic analysis and provide unblocked online video watching and social networking.enAnonymityInformation LeakageTraffic AnalysisThesis or Dissertation