Structure preserving anonymization of router configuration data

  • Authors:
  • David A. Maltz;Jibin Zhan;Gísli Hjálmtysson;Albert Greenberg;Jennifer Rexford;Geoffrey G. Xie;Hui Zhang

  • Affiliations:
  • Microsoft Research;Conviva Networks;Thule Investments;Microsoft Research;Dept. of Computer Science, Princeton University;Dept. of Computer Science, Naval Postgraduate School;Dept. of Computer Science, Carnegie Mellon University.

  • Venue:
  • IEEE Journal on Selected Areas in Communications - Special issue on network infrastructure configuration
  • Year:
  • 2009

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Abstract

A repository of router configuration files from production networks would provide the research community with a treasure trove of data about network topologies, routing designs, and security policies. However, configuration files have been largely unobtainable precisely because they provide detailed information that could be exploited by competitors and attackers. This paper describes a method for anonymizing router configuration files by removing all information that connects the data to the identity of the underlying network, while still preserving the structure of information that makes the data valuable to networking researchers. Anonymizing configuration files has unusual requirements, including preserving relationships between elements of data, anonymizing regular expressions, and robustly coping with more than 200 versions of the configuration language. Conventional tools and techniques are poorly suited to the problem. Our anonymization method has been validated with a major carrier, earning unprivileged researchers access to the configuration files of thousands of routers in hundreds of networks. Through example analysis, we demonstrate that the anonymized data retains the key properties of the network design. The paper sets out techniques that could be used in an attempt to break the anonymization, and it concludes our anonymization techniques are most applicable to enterprise networks, because the large number of enterprises and the difficulty of probing them from the outside make it hard to recognize an anonymized network based solely on publicly-available information about its topology or configuration. When applied to backbone networks, which are few in number and many of whose properties can be publicly measured, the anonymization might be broken by fingerprinting techniques described in this paper.