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Extracting association patterns in network communications.

Javier Portela1, Luis Javier García Villalba2, Alejandra Guadalupe Silva Trujillo3,4

  • 1Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Information Technology and Computer Science, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, Madrid 28040, Spain. jportela@estad.ucm.es.

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Summary

This study introduces a new statistical attack to uncover user relationships in anonymous communication systems. By analyzing message counts, it effectively identifies sender-receiver links, enhancing network security analysis.

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Area of Science:

  • Computer Science
  • Network Security
  • Cryptography

Background:

  • Anonymity systems like mixes protect message content, patterns, and sender-receiver links from observers.
  • Statistical disclosure attacks aim to de-anonymize users by analyzing metadata in communication networks.
  • Existing attacks often rely on specific assumptions, limiting their applicability.

Purpose of the Study:

  • To develop a novel global statistical disclosure attack to detect user relationships.
  • To create a flexible framework applicable to diverse communication data, including email and social networks.
  • To provide a method for simultaneously identifying relationships between all user pairs.

Main Methods:

  • Utilizing a new modeling framework based on contingency tables.
  • Employing a classification scheme derived from combinatoric solutions of retrieved rounds.
  • Analyzing only the number of messages sent and received by each user per round, grouped by the anonymity system.

Main Results:

  • The proposed method successfully detects relationships between users in network communications.
  • The contingency table framework offers more flexible assumptions than previous methods.
  • The approach is adaptable for automatic application to various data types, such as email and social networks.

Conclusions:

  • This work presents an effective global statistical attack for de-anonymizing communication networks.
  • The flexible modeling framework enhances the applicability of statistical disclosure attacks.
  • Simultaneous relationship detection across all user pairs addresses data dependencies effectively.