Modeling Tor Network Growth by Extrapolating Consensus Data

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

  • Christoph Döpmann - , Technische Universität Berlin (Autor:in)
  • Florian Tschorsch - , Technische Universität Berlin (Autor:in)

Abstract

Since the Tor network is evolving into an infrastructure for anonymous communication, analyzing the consequences of network growth is becoming more relevant than ever. In particular, adding large amounts of resources may have unintentional consequences for the system performance as well as security. To this end, we contribute a methodology for the analysis of scaled Tor networks that enables researchers to leverage real-world network data. Based on historical network snapshots (consensuses), we derive and implement a model for methodically scaling Tor consensuses. This allows researchers to apply established research methods to scaled networks. We validate our model based on historical data, showing its applicability. Furthermore, we demonstrate the merits of our data-driven approach by conducting a simulation study to identify performance impacts of scaling Tor.

Details

OriginalspracheEnglisch
TitelARES 2023 - 18th International Conference on Availability, Reliability and Security, Proceedings
Seiten29:1-29:7
ISBN (elektronisch)9798400707728
PublikationsstatusVeröffentlicht - 29 Aug. 2023
Peer-Review-StatusJa
Extern publiziertJa

Externe IDs

Scopus 85169699224

Schlagworte

Schlagwörter

  • Tor, methods/tools, overlays, scalability, security and privacy