Provable Anonymity for Networks of Mixes

By Marek Klonowski, Mirosław Kutyłowski. 2005

16 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ú. Erlingsson
2. Ananth Raghunathan
3. Ilya Mironov
4. Jonathan Ullman
5. David Lie
6. Petros Maniatis
7. Julien Tinnés
8. Mitch Rudominer

Citations in the corpus

• Prochlo: Strong Privacy for Analytics in the Crowd (2017, background): “Unfortunately, for a safe security parameter = 2−64, a significant number of rounds is required [40].”
• M2R: Enabling Stronger Privacy in MapReduce Computation (2015, methodology): “Our design achieves privacy using a cascaded mix network (or cascaded-mix) to securely shuffle tuples [30].”
• PROCHLO: Strong Privacy for Analytics in the Crowd (2017, background): “Unfortunately, for a safe security parameter = 2−64, a significant number of rounds is required [40].”
• Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute (2017, background): “Another (arguably simpler) construction of S could be based on Chaum’s mix-network [16], which achieves statistical indistinguishability, but requires large trusted memory [30].”