scispace - formally typeset
G

George Danezis

Researcher at University College London

Publications -  213
Citations -  12903

George Danezis is an academic researcher from University College London. The author has contributed to research in topics: Anonymity & Traffic analysis. The author has an hindex of 59, co-authored 209 publications receiving 11516 citations. Previous affiliations of George Danezis include University of Cambridge & Microsoft.

Papers
More filters

Private client-side profiling with random forests and hidden markov models

TL;DR: In this article, the authors propose privacy-preserving profiling techniques, in which users perform the profiling task locally, reveal to service providers the result and prove its correctness, and apply how their approach applies to tasks of both classification and pattern recognition.

The dining freemasons (security protocols for secret societies)

TL;DR: This work discusses membership testing problems and solutions, set in the context of security authentication protocols, and presents new building blocks which could be used to generate secret society protocols more robustly and generically, including the lie channel and the compulsory arbitrary decision model.
Book ChapterDOI

Chaffinch: Confidentiality in the Face of Legal Threats

TL;DR: It is shown how the design and rationale of a practical system for passing confidential messages may have some resilience to the type of legal attack inherent in the UK's Regulation of Investigatory Powers Act.
Proceedings ArticleDOI

LiLAC: Lightweight Low-Latency Anonymous Chat

TL;DR: The design and engineering of LiLAC, a Lightweight Low-latency Anonymous Chat service, is described, that offers both strong anonymity and a lightweight client-side presence and leads to a key trade-off between the system's bandwidth overhead and end-to-end delay along the circuit.
Journal Article

Vida: How to use Bayesian inference to de-anonymize persistent communications

TL;DR: In this paper, the Vida family of abstractions of anonymous communication systems, model them probabilistically and apply Bayesian inference to extract patterns of communications and user profiles, and evaluate the Red-Blue model to find that it is competitive with other established long-term traffic analysis attacks, while additionally providing reliable error estimates.