Welcome to the resource topic for 2023/199
Title:
MixFlow: Assessing Mixnets Anonymity with Contrastive Architectures and Semantic Network Information
Authors: Reyhane Attarian, Esfandiar Mohammadi, Tao Wang, Emad Heydari Beni
Abstract:In this paper, we propose the MixFlow model, an approach for analyzing the unobservability and unlinkability of instant messaging in Loopix Mix networks. The MixFlow model utilizes contrastive architectures and loss functions inspired by DNA sequence analysis in bioinformatics to identify semantic relationships between entry and exit flows, even after applying significant transformations such as poisson mixing delay and cover traffic. We use the MixFlow model to evaluate the resistance of Loopix Mix networks against a global passive adversary with the ability to control both ends of the network and infer real messages from cover messages. Our experimental results demonstrate that the MixFlow model is highly effective in linking end-to-end flows with a detection rate of over 90%, challenging the common belief that adding poisson mixing delay and cover traffic can obscure the metadata patterns and relationships between communicating parties. Our findings have important implications for existing Poisson-mixing techniques and open up new opportunities for analyzing the anonymity and unlinkability of communication protocols.
ePrint: https://eprint.iacr.org/2023/199
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .