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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What Makes Phishing Simulation Campaigns (Un)Acceptable? A Vignette Experiment

Jasmin Schwab (German Aerospace Center (DLR)), Alexander Nussbaum (University of the Bundeswehr Munich), Anastasia Sergeeva (University of Luxembourg), Florian Alt (University of the Bundeswehr Munich and Ludwig Maximilian University of Munich), and Verena Distler (Aalto University)

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Reinforcement Unlearning

Dayong Ye (University of Technology Sydney), Tianqing Zhu (City University of Macau), Congcong Zhu (City University of Macau), Derui Wang (CSIRO’s Data61), Kun Gao (University of Technology Sydney), Zewei Shi (CSIRO’s Data61), Sheng Shen (Torrens University Australia), Wanlei Zhou (City University of Macau), Minhui Xue (CSIRO's Data61)

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