Mixnets provide strong meta-data privacy and recent academic research and industrial projects have made strides in making them more secure, performance, and scalable. In this paper, we focus our work on stratified Mixnets -- a popular design with real-world adoption -- and identify that there still exist heretofore inadequately explored practical aspects such as: relay sampling and topology placement, network churn, and risks due to real-world usage patterns. We show that, due to the lack of incorporating these aspects, Mixnets of this type are far more susceptible to user deanonymization than expected. In order to reason and resolve these issues, we model Mixnets as a three-stage ``Sample-Placement-Forward'' pipeline, and using the results of our evaluation propose a novel Mixnet design, Bow-Tie. Bow-Tie mitigates user deanonymization through a novel adaption of Tor's guard design with an engineered guard layer and client guard-logic for stratified mixnets. We show that Bow-Tie has significantly higher user anonymity in the dynamic setting, where the Mixnet is used over a period of time, and is no worse in the static setting, where the user only sends a single message. We show the necessity of both the guard layer and client guard-logic in tandem as well as their individual effect when incorporated into other reference designs. Ultimately, Bow-Tie is a significant step towards addressing the gap between the design of Mixnets and practical deployment and wider adoption because it directly addresses real-world user and Mixnet operator concerns.
翻译:混合网提供了强大的元数据隐私,最近学术研究和工业项目在使其更加安全、性能和可缩放性方面取得了长足的进步。在本文件中,我们把工作重点放在分层的混合网上,这是现实世界采用的一种流行设计,并查明迄今为止仍然存在着尚未充分探讨的实际方面,例如:中继取样和地形定位、网络圈圈以及现实世界使用模式造成的风险。我们表明,由于缺乏纳入这些方面,这种类型的混合网比预期的更易成为用户脱匿名化的实用性。为了解释和解决这些问题,我们把混合网作为三阶段的“Sample-Place-Forward”管道模型,我们用我们的评价结果来提出新的混合网设计、网络图案、网络网圈圈和网络使用模式的新调整托尔的警卫设计、设计警卫层和客户保护-logical-logical 地址比预期的要广泛得多。我们显示,在用户流网流流流流中,在用户流层流流流流流中,我们只是用一个更清晰的用户匿名的用户流流流流流流流流流中,因此,在用户流流流流流流流流流流流流流流流流的用户流的用户流流中,而流流流流流流流流流流流流流流流的用户流流流流流流流流流流流流流流流流流流流流流中, 流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流信息流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流