I am a Doctoral Researcher in the Information Security Group at University College London, advised by Prof. George Danezis and Dr. Emiliano De Cristofaro. My research interests lie in Systems Security, Machine Learning and Privacy.
Currently, I am studying how deep neural networks could be used to launch privacy attacks (e.g., website fingerprinting), as well as potential countermeasures to such practices. I am also investigating potential defenses based on adversarial samples produced by GANs. Recently, we concluded our work on how machine learning models can be used to launch and amplify side-channel attacks against secure chips (see Location, location, location).
Besides these, I have worked on a variety of other projects (see Publications). Snappy is a fast-payment solution for slow permisionless blockchains. Myst is a high-assurance cryptographic hardware prototype which was the first trojan-resilient deployment that achieved performance similar to that of conventional hardware security modules used in production (CSAW 2018 Competition Finalist). Our study on ultrasound tracking received wide-spread attention and is considered the seminal work in this area. With Petr Svenda, we released the first open-source cryptographic library for JavaCards. Finally, I recently published a stream of papers studying market fairness & manipulation as security problems.
For my research, I have been kindly supported by Oasis Labs, Binance Labs, the Allan & Nesta Ferguson Charitable Trust and the UCL Public Engagement Unit. I was also honored to be in the 10-of-200 young researchers' list by the Heidelberg Laureate Forum, a recipient of the Werner Romberg Grant, as well as a finalist at the CSAW Europe 2018 Applied Research Competition.
Vasilios Mavroudis
v.mavroudis at ucl.ac.ukComputer Science Department
University College London
Gower Street
London WC1E 6BT
United Kingdom
