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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Black-box Membership Inference Attacks against Fine-tuned Diffusion Models

Yan Pang (University of Virginia), Tianhao Wang (University of Virginia)

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Work-in-Progress: Towards Browser-Based Consent Management

Gayatri Priyadarsini Kancherla and Abhishek Bichhawat (Indian Institute of Technology Gandhinagar)

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AI-Assisted RF Fingerprinting for Identification of User Devices in...

Aishwarya Jawne (Center for Connected Autonomy & AI, Florida Atlantic University), Georgios Sklivanitis (Center for Connected Autonomy & AI, Florida Atlantic University), Dimitris A. Pados (Center for Connected Autonomy & AI, Florida Atlantic University), Elizabeth Serena Bentley (Air Force Research Laboratory)

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