Projects
Enabling trust models for differential privacy
Exploring novel trust models and how much extra utility can be gained from them
Statistical secret sharing
Developing methodology for multiple parties to securely share and unify their statistical inference, without having to reveal their inference or underlying data
People
Rafiah Patel
Enrichment Student
Torty Sivill
Doctoral Student
Dr Louis Aslett
Health Programme Fellow
Professor Andrew Dowsey
Turing Fellow
Professor Emiliano De Cristofaro
Turing Fellow
Professor Ser-Huang Poon
Turing Fellow
David Butler
Doctoral Student
Dr Adrià Gascón
Visiting Researcher
Professor David Aspinall
Turing FellowEvents
Modern cryptography in the context of electric vehicles
Wednesday 11 Nov 2020
Time: 16:00 - 17:00
CIFAR-UKRI-CNRS AI & society: From principles to practice
Monday 03 Jun 2019 - Tuesday 04 Jun 2019
Time: 09:00 - 18:30
Distributed ledgers for engineered systems: hype or hope?
Wednesday 27 Sep 2017
Time: 18:37
Turing Lecture: Statistics, decision-making and privacy
Monday 05 Dec 2016
Time: 13:30 - 17:00
Articles
Turing researchers work to ensure security and privacy of COVID-19 ‘immunity passports’
Turing researchers have developed a Secure AntiBody Certificate system.
Thursday 28 May 2020
Publications and software
SecureABC: Secure antibody certificates for COVID-19
COVID-19 has resulted in unprecedented social distancing policies being enforced worldwide. As governments urgently...
Chris Hicks*, David Butler*, Carsten Maple, Jon Crowcroft. SecureABC: Secure AntiBody Certificates for COVID-19. CoRR, abs/2005.11833. 2020.
TraceSecure: Towards privacy preserving contact tracing
Contact tracing is being widely employed to combat the spread of COVID-19. Many apps...
James Bell and David Butler and Chris Hicks and Jon Crowcroft. TraceSecure: Towards Privacy Preserving Contact Tracing. CoRR, abs/2004.04059. 2020.
Differentially private health tokens for estimating COVID-19 risk
In the fight against Covid-19, many governments and businesses are in the process of...
David Butler*, Chris Hicks*, James Bell, Carsten Maple, Jon Crowcroft. Differentially Private Health Tokens for Estimating COVID-19 Risk. appeared in NDSS CoronaDef 2021.
Formalising Oblivious Transfer in the Semi-Honest and Malicious Model in CryptHOL
Multi-Party Computation (MPC) allows multiple parties to compute a function together while keeping their...
Formalising Oblivious Transfer in the Semi-Honest and Malicious Model in CryptHOL. David Butler, David Aspinall and Adria Gascon. To appear in CPP 2020.
Formalising Sigma-Protocols and Commitment Schemes using CryptHOL
Machine-checked proofs of security are important to increase the rigour of provable security. In...
David Butler, Andreas Lochbihler, David Aspinall, Adria Gascon, Formalising Sigma-Protocols and Commitment Schemes using CryptHOL, Cryptography ePrint Archive, Report 2019/1185, https://eprint.iacr.org/2019/1185.pdf
Artificial intelligence in finance
Artificial intelligence (AI) is rapidly transforming the global financial services industry. As a group...
Buchanan, Bonnie. (2019, April 02). Artificial intelligence in finance. Zenodo. http://doi.org/10.5281/zenodo.2612537
Blind Justice: Fairness with Encrypted Sensitive Attributes
Recent work has explored how to train machine learning models which do not discriminate...
Kilbertus, N., Gascon, A., Kusner, M., Veale, M., Gummadi, K.P. & Weller, A.. (2018). Blind Justice: Fairness with Encrypted Sensitive Attributes. Proceedings of the 35th International Conference on Machine Learning, in PMLR 80:2635-2644
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Machine learning methods are widely used for a variety of prediction problems. Prediction as...
Sanyal, A., Kusner, M.J., Gascón, A., & Kanade, V. (2018). TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. CoRR, abs/1806.03461.
Interest groups
Machine learning for radio frequency applications
How do we generate robust, real-time machine learning algorithms with integrated hardware and software for radio frequency applications?
Trustworthy digital identity
How can we understand, evaluate and advance the trustworthiness of digital identity systems?
