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Mixnets on a tightrope: quantifying the leakage of mix networks using a provably optimal heuristic adversary
University of Lübeck, Germany.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
University of Lübeck, Germany.
University of Lübeck, Germany.
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2025 (English)In: 2025 IEEE Symposium on Security and Privacy (SP): conference proceedings, 2025Conference paper, Published paper (Refereed)
Abstract [en]

Mixnets are widely believed to hide communication metadata of individuals. We show that there are various pitfalls when designing mixnet topologies and routing strategies, in particular when choosing mixnets with low delays. We introduce a tool that empirically evaluates such leakage in mixnets and show that this tool precisely estimates this leakage for recipient anonymity, up to an error introduced by sampling. First, we introduce a novel generic attack strategy that we even prove to be optimal for breaking recipient anonymity. In contrast to prior work, our attack strategy incorporates the severity of each observation's leakage, via its so-called privacy loss. Second, our tool provides a lower bound on an attacker's advantage against recipient anonymity by sampling a large set of observations; if a significant number of observations with high privacy loss is observed, the tool outputs a lower bound on the leakage by providing a lower bound on the mass of the tail of the distribution of privacy losses. From the literature, we study the topology and routing strategies of the Karaoke and Atom protocols, provide bounds on their leakage, and recommend design choices based on the analysis.

Place, publisher, year, edition, pages
2025.
Series
Proceedings of the IEEE Symposium on Security and Privacy, ISSN 2375-1207
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-238268DOI: 10.1109/SP61157.2025.00233Scopus ID: 2-s2.0-105009321462ISBN: 979-8-3315-2236-0 (print)OAI: oai:DiVA.org:umu-238268DiVA, id: diva2:1955048
Conference
2025 IEEE Symposium on Security and Privacy (SP), San Fransisco, USA, May 12-15, 2025
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2025-04-28 Created: 2025-04-28 Last updated: 2025-07-08Bibliographically approved

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Das, Debajyoti

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf