Hussein Darir (University of Illinois Urbana-Champaign), Geir Dullerud (University of Illinois Urbana-Champaign), Nikita Borisov (University of Illinois Urbana-Champaign)

We present ProbFlow, a probabilistic programming approach for estimating relay capacities in the Tor network. We refine previously derived probabilistic model of the network to take into account more of the complexity of the real-world Tor network. We use this model to perform inference in a probabilistic programming language called NumPyro which allows us to overcome the analytical barrier present in purely analytical approach. We integrate the implementation of ProbFlow to the current implementation of capacity estimation algorithms in the Tor network. We demonstrate the practical benefits of ProbFlow by simulating it in flow-based Python simulator and packet-based Shadow simulations, the highest fidelity simulator available for the Tor network. In both simulators, ProbFlow provides significantly more accurate estimates that results in improved user performance, with average download speeds increasing by 25% in the Shadow simulations.

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Real Threshold ECDSA

Harry W. H. Wong (The Chinese University of Hong Kong), Jack P. K. Ma (The Chinese University of Hong Kong), Hoover H. F. Yin (The Chinese University of Hong Kong), Sherman S. M. Chow (The Chinese University of Hong Kong)

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REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder...

Wenjie Qu (Huazhong University of Science and Technology), Jinyuan Jia (University of Illinois Urbana-Champaign), Neil Zhenqiang Gong (Duke University)

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Your Router is My Prober: Measuring IPv6 Networks via...

Long Pan (Tsinghua University), Jiahai Yang (Tsinghua University), Lin He (Tsinghua University), Zhiliang Wang (Tsinghua University), Leyao Nie (Tsinghua University), Guanglei Song (Tsinghua University), Yaozhong Liu (Tsinghua University)

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