CLAPS: Client-Location-Aware Path Selection in Tor
Autor: | Rochet, Florentin, Ryan Wails, Aaron Johnson, Prateek Mittal, Pereira, Olivier, Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security |
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Přispěvatelé: | UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique |
Rok vydání: | 2020 |
Předmět: |
050101 languages & linguistics
Guard (information security) anonymity Optimization problem business.industry Computer science 05 social sciences Bandwidth (signal processing) 02 engineering and technology tor onion routing Adversary law.invention Relay law Path (graph theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Onion routing business Computer network Anonymity |
Zdroj: | CCS |
Popis: | Much research has investigated improving the security and performance of Tor by having Tor clients choose paths through the network in a way that depends on the client's location. However, this approach has been demonstrated to lead to serious deanonymization attacks. Moreover, we show how in some scenarios it can lead to significant performance degradation. For example, we demonstrate that using the recently-proposed Counter-RAPTOR system when guard bandwidth isn't abundant could increase median download times by 28.7%. We propose the CLAPS system for performing client-location-aware path selection, which fixes the known security and performance issues of existing designs. We experimentally compare the security and performance of CLAPS to Counter-RAPTOR and DeNASA. CLAPS puts a strict bound on the leakage of information about the client's location, where the other systems could completely reveal it after just a few connections. It also guarantees a limit on the advantage that an adversary can obtain by strategic relay placement, which we demonstrate to be overwhelming against the other systems. Finally, due to a powerful formalization of path selection as an optimization problem, CLAPS is approaching or even exceeding the original goals of algorithms to which it is applied, while solving their known deficiencies. |
Databáze: | OpenAIRE |
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