Quantifying Information Exposure in Internet Routing

Published

September 6, 2018

Author(s)

Peter M. Mell, Assane Gueye, Christopher A. Schanzle

Abstract

Data sent over the Internet can be monitored and manipulated by intermediate entities in the data path from the source to the destination. For unencrypted communications (and some encrypted communications with known weaknesses), eavesdropping and man-in-the-middle attacks are possible. For encrypted communication, the identification of the communicating endpoints is still revealed. In addition, encrypted communications may be stored until such time as newly discovered weaknesses in the encryption algorithm or advances in computer hardware render them readable by attackers. In this work, we evaluate both advertised and observed routes through the Internet and measure the extent to which communications between pairs of countries are exposed to other countries. We use both physical router geolocation as well as the country of registration of the companies owning each router. We find a high level of information exposure; even physically adjacent countries use routes that involve many other countries. We also found that countries that are well `connected' tend to be more exposed. Our analysis indicates that there exists a tradeoff between robustness and information exposure in the current Internet. We run experiments on all countries, but focus on providing results for the United States and Brazil.

Proceedings Title

The 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications

Conference Dates

August 1-3, 2018

Conference Location

New York, NY

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/TrustCom/BigDataSE.2018.00237

Keywords

Measurement, Privacy, Internet

Citation

Mell, P. , Gueye, A. and Schanzle, C. (2018), Quantifying Information Exposure in Internet Routing, The 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications, New York, NY, [online], https://doi.org/10.1109/TrustCom/BigDataSE.2018.00237 (Accessed November 23, 2024)