Representing Network Trust and Using It to Improve Anonymous Communication

Abstract

Motivated by the effectiveness of correlation attacks against Tor, the censorship arms race, and observations of malicious relays in Tor, we propose that Tor users capture their trust in network elements using probability distributions over the sets of elements observed by net- work adversaries. We present a modular system that allows users to efficiently and conveniently create such distributions and use them to improve their security. The major components of this system are (i) an ontology of network-element types that represents the main threats to and vulnerabilities of anonymous communication over Tor, (ii\) a formal language that allows users to naturally express trust beliefs about network elements, and (iii) a conversion procedure that takes the ontology, public information about the network, and user beliefs written in the trust language and produce a Bayesian Belief Network that represents the probability distribution in a way that is concise and easily sampleable. We also present preliminary experimental results that show the distribution produced by our system can improve security when employed by users; further improvement is seen when the system is employed by both users and services.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2014
Accession Number
ADA621866

Entities

People

  • Aaron D. Jaggard
  • Aaron M. Johnson
  • Joan Feigenbaum
  • Paul Syverson

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Anonymous Communications
  • Autonomous Systems
  • Cables
  • Corporations
  • Internet Routing
  • Language
  • Models
  • Network Protocols
  • Observation
  • Ontologies
  • Operating Systems
  • Probability
  • Probability Distributions
  • Security
  • Translations
  • Underwater Cables

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Statistical inference.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference