Samuel Maddock
About Me
I am a third-year PhD student supervised by Prof. Graham Cormode and Prof. Carsten Maple (WMG). Before starting my PhD I graduated with a first-class MEng in Discrete Mathematics here at the University of Warwick.
During the Summer of 2022, I was a research intern at Meta AI supervised by Pierre Stock and Alexandre Sablayrolles.
Research Interests
My research interests lie at the intersection of computer security, data privacy and machine learning. I am mainly interested in the area of privacy-preserving machine learning (PPML) which seeks to develop ML algorithms to learn privately and securely from data which is often highly distributed. This includes areas such as Differential Privacy (DP), Federated Learning (FL) and Federated Analytics.
Preprints
- Samuel Maddock, Graham Cormode, Carsten Maple. "FLAIM: AIM-based Synthetic Data Generation in the Federated Setting", 2023
Publications
- Samuel Maddock, Alexandre Sablayrolles and Pierre Stock. "CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning". The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. (Slides, Poster)
- Samuel Maddock, Graham Cormode, Tianhao Wang, Carsten Maple and Somesh Jha. "Federated Boosted Decision Trees with Differential Privacy". In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS ’22), November 7–11, 2022, Los Angeles, CA, USA. (Slides)
- Graham Cormode, Samuel Maddock, and Carsten Maple. "Frequency estimation under local differential privacy." Proceedings of the VLDB Endowment 14.11 (2021): 2046-2058. (Slides , Poster)
Teaching
I have been a teaching assistant for the following modules:
