|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Bayesian Inference and Traffic Analysis
If you have a question about this talk, please contact Andrew Lewis.
Traffic analysis attacks on anonymity networks were for long based on heuristics that allow an attacker to uncover communication partners under specific assumptions. However, slight changes in the model would render the methods useless. We present a general model for the analysis of mix networks which captures characteristics of anonymity systems subject to constraints while being able to accommodate most previously proposed attacks. Furthermore, we show how this model can be used to obtain the probabilities of who speaks with whom through the use of Bayesian Inference techniques and Markov Chain Monte Carlo simulations.
This talk is part of the Computer Laboratory Security Seminar series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsFinance and Accounting Subject Group Directions in Research Talks CJCR
Other talksAerobic Oxidation Reactions for Organic Chemical Synthesis The Science of Pain and its Management 2016 From Sensory Perception to Foraging Decision Making, the Bat's Point of View Mechanisms of lymphomagenesis: Developmental origins of a paediatric cancer Data Dissemination: A Survey of Recent Approaches, Challenges, and Connections to Data Linkage Moving Forward with Stem Cell Therapy