Publication | Closed Access
(Nothing else) MATor(s)
49
Citations
27
References
2014
Year
Unknown Venue
Artificial IntelligenceEngineeringInformation SecurityPresent MatorNetwork AnalysisUser AnonymityData ScienceData AnonymizationPrivacy SystemPrivacy-preserving CommunicationSymbolic TechniqueSocial Network AnalysisMachine SystemsData PrivacyComputer SciencePrivacy AnonymityData SecurityCryptographyRigorous Anonymity BoundsNetwork ScienceDecentralized PrivacyComputer Algebra
In this paper we present MATor: a framework for rigorously assessing the degree of anonymity in the Tor network. The framework explicitly addresses how user anonymity is impacted by real-life characteristics of actually deployed Tor, such as its path selection algorithm, Tor consensus data, and the preferences and the connections of the user. The anonymity assessment is based on rigorous anonymity bounds that are derived in an extension of the AnoA framework (IEEE CSF 2013). We show how to apply MATor on Tor's publicly available consensus and server descriptor data, thereby realizing the first real-time anonymity monitor. Based on experimental evaluations of this anonymity monitor on Tor Metrics data, we propose an alternative path selection algorithm that provides stronger anonymity guarantees without decreasing the overall performance of the Tor network.
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