Mahdi Rahimi (KU Leuven), Piyush Kumar Sharma (University of Michigan), Claudia Diaz (KU Leuven)

Mixnets are a type of anonymous communication system designed to provide network privacy to users. They route client messages through multiple hops, with each hop (mix)
perturbing the traffic patterns, thus making message tracing difficult for a network adversary. However, privacy in mixnets comes at the cost of increased latency, limiting the applications
that are usable when accessed through a mixnet. In this work we present LAMP, a set of routing approaches tailored for minimizing the propagation latency in mixnets with minimal
impact on anonymity. The design of these approaches is grounded in practical deployment considerations making them lightweight, easy to integrate with existing deployed mixnets and computationally realistic. We evaluate the proposed approaches using latency data from the deployed Nym mixnet and demonstrate that LAMP can reduce latency by a factor of 7.5 (from 153.4ms to 20ms) while maintaining high anonymity. LAMP even outperforms the
state-of-the-art system LARMix, providing 3× better latency-anonymity tradeoffs and significantly reducing the computational overhead by ≈ 13900× in comparison to LARMix.

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Rethink Custom Transformers for Binary Analysis

Heng Yin, Professor, Department of Computer Science and Engineering, University of California, Riverside

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Rondo: Scalable and Reconfiguration-Friendly Randomness Beacon

Xuanji Meng (Tsinghua University), Xiao Sui (Shandong University), Zhaoxin Yang (Tsinghua University), Kang Rong (Blockchain Platform Division,Ant Group), Wenbo Xu (Blockchain Platform Division,Ant Group), Shenglong Chen (Blockchain Platform Division,Ant Group), Ying Yan (Blockchain Platform Division,Ant Group), Sisi Duan (Tsinghua University)

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