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1. RPM: Robust Anonymity at Scale

   https://doi.org/10.56553/POPETS-2023-0057  

   Lu, Donghang; Kate, Aniket (April 2023, Proceedings on Privacy Enhancing Technologies)

   Mazurek, Michelle L; Sherr, Micah. (Ed.)

   This work presents RPM, a scalable anonymous communication protocol suite using secure multiparty computation (MPC) with the offline-online model. We generate random, unknown permutation matrices in a secret-shared fashion and achieve improved (online) performance and the lightest communication and computation overhead for the clients compared to the state of art robust anonymous communication protocols. Using square-lattice shuffling, we make our protocol scale well as the number of clients increases. We provide three protocol variants, each targeting different input volumes and MPC frameworks/libraries. Besides, due to the modular design, our protocols can be easily generalized to support more MPC functionalities and security properties as they get developed. We also illustrate how to generalize our protocols to support two-way anonymous communication and secure sorting. We have implemented our protocols using the MP-SPDZ library suit and the benchmark illustrates that our protocols achieve unprecedented online phase performance with practical offline phases. 

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2. Traffic Analysis Resistant Network (TARN) Anonymity Analysis

   https://doi.org/10.1109/ICNP.2019.8888134  

   Tusing, Nathan; Oakley, Jonathan; Barrineau, Geddings; Yu, Lu; Wang, Kuang-Ching; Brooks, Richard R. (October 2019, Proceedings of 2019 IEEE 27th International Conference on Network Protocols (ICNP))

   null (Ed.)
3. Traffic Analysis Resistant Network (TARN) Anonymity Analysis

   Nathan Tusing, Jonathan Oakley (October 2019, Proceedings for the 2019 Midscale Education and Research Infrastructure Tools (MERIT) Workshop)
4. Privacy and anonymity for multilayer networks: A reflection

   Abhishel Santraa, Kiran Mukunda (July 2023, IEEEBigDataService)

   AbstractÐPrivacy of data as well as providing anonymization of data for various kinds of analysis have been addressed in the context of tabular transactional data which was mainstream. With the advent of the Internet and social networks, there is an emphasis on using different kinds of graphs for modeling and analysis. In addition to single graphs, the use of MultiLayer Networks (or MLNs) for modeling and analysis is becoming popular for complex data having multiple types of entities and relationships. They provide a better understanding of data as well as flexibility and efficiency of analysis. In this article, we understand the provenance of data privacy and some of the thinking on extending it to graph data models. We will focus on the issues of data privacy for models that are different from traditional data models and discuss alternatives. We will also consider privacy from a visualization perspective as we have developed a community Dashboard for MLN generation, analysis, and visualization based on our research. 

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5. Comprehensive Anonymity Trilemma: User Coordination is not enough

   https://doi.org/10.2478/popets-2020-0056  

   Das, Debajyoti; Meiser, Sebastian; Mohammadi, Esfandiar; Kate, Aniket (July 2020, Proceedings on Privacy Enhancing Technologies)

   null (Ed.)

   Abstract For anonymous communication networks (ACNs), Das et al. recently confirmed a long-suspected trilemma result that ACNs cannot achieve strong anonymity, low latency overhead and low bandwidth overhead at the same time. Our paper emanates from the careful observation that their analysis does not include a relevant class of ACNs with what we call user coordination where users proactively work together towards improving their anonymity. We show that such protocols can achieve better anonymity than predicted by the above trilemma result. As the main contribution, we present a stronger impossibility result that includes all ACNs we are aware of. Along with our formal analysis, we provide intuitive interpretations and lessons learned. Finally, we demonstrate qualitatively stricter requirements for the Anytrust assumption (all but one protocol party is compromised) prevalent across ACNs. 

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