CLAPS: Client-Location-Aware Path Selection in Tor

CLAPS: Client-Location-Aware Path Selection in Tor
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CLAPS:Tor 中的客户端位置感知路径选择

DOI:
10.1145/3372297.3417279
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发表时间:
2020
期刊:
ACM Conference on Computer and Communication Security (CCS
影响因子:
--
通讯作者:
Pereira, Olivier
Pereira, Olivier
中科院分区:
--
文献类型:
--
作者:
Rochet, Florentin;Wails, Ryan;Johnson, Aaron;Mittal, Prateek;Pereira, Olivier

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许多研究都通过让 Tor 客户端根据客户端位置选择网络路径来提高 Tor 的安全性和性能。然而,这种方法已被证明会导致严重的去匿名化攻击。此外,我们还展示了在某些情况下它如何导致性能显着下降。例如,我们证明,当保护带宽不足时,使用最近提出的 Counter-RAPTOR 系统可以将下载时间中位数增加 28.7%。我们提出了用于执行客户端位置感知路径选择的 CLAPS 系统,该系统修复了现有设计的已知安全和性能问题。我们通过实验将 CLAPS 与 Counter-RAPTOR 和 DeNASA 的安全性和性能进行比较。 CLAPS 对有关客户端位置信息的泄露进行了严格限制,其他系统只需几次连接就可以完全泄露该信息。它还保证了对手通过战略中继布局获得的优势受到限制,我们证明这对其他系统具有压倒性优势。最后,由于路径选择作为优化问题的强大形式化,CLAPS 正在接近甚至超越其所应用的算法的原始目标,同时解决其已知的缺陷。
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.
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