SoK on Performance Bounds in Anonymous Communication
November 17, 2020 Β· Declared Dead Β· π WPES@CCS
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Christiane Kuhn, Friederike Kitzing, Thorsten Strufe
arXiv ID
2011.08536
Category
cs.CR: Cryptography & Security
Citations
1
Venue
WPES@CCS
Last Checked
5 months ago
Abstract
Communicating anonymously comes at a cost - and large communities have been in a constant tug-of-war between the development of faster protocols, and the improvement of security analyses. Thereby more intricate privacy goals emerged and more detailed bounds on the minimum overhead necessary to achieve them were proven. The entanglement of requirements, scenarios, and protocols complicates analysis, and the published results are hardly comparable, due to deviating, yet specific choices of assumptions and goals (some explicit, most implicit). In this paper, we systematize the field by harmonizing the models, comparing the proven performance bounds, and contextualizing these theoretical results in a broad set of proposed and implemented systems. By identifying inaccuracies, we demonstrate that the attacks, on which the results are based, indeed break much weaker privacy goals than postulated, and tighten the bounds along the way. We further show the equivalence of two seemingly alternative bounds. Finally, we argue how several assumptions and requirements of the papers likely are of limited applicability in reality and suggest relaxations for future work.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted