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.

View More Papers

Logical Maneuvers: Detecting and Mitigating Adversarial Hardware Faults in...

Fatemeh Khojasteh Dana, Saleh Khalaj Monfared, Shahin Tajik (Worcester Polytechnic Institute)

Read More

Privacy-Enhancing Technologies Against Physical-Layer and Link-Layer Device Tracking: Trends,...

Apolline Zehner (Universite libre de Bruxelles), Iness Ben Guirat (Universite libre de Bruxelles), Jan Tobias Muhlberg (Universite libre de Bruxelles)

Read More

LADDER: Multi-Objective Backdoor Attack via Evolutionary Algorithm

Dazhuang Liu (Delft University of Technology), Yanqi Qiao (Delft University of Technology), Rui Wang (Delft University of Technology), Kaitai Liang (Delft University of Technology), Georgios Smaragdakis (Delft University of Technology)

Read More

Beyond Classification: Inferring Function Names in Stripped Binaries via...

Linxi Jiang (The Ohio State University), Xin Jin (The Ohio State University), Zhiqiang Lin (The Ohio State University)

Read More