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Information Leakage Measurement and Prevention in Anonymous Traffic
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Information Leakage Measurement and Prevention in Anonymous Traffic. |
| 개인저자 | Li, Shuai. |
| 단체저자명 | University of Minnesota. Computer Science. |
| 발행사항 | [S.l.]: University of Minnesota., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 87 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781088300244 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Hopper, Nick. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | 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. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
