Selected Publications

A new general framework for privacy preserving deep learning. Focuses on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. The abstraction is used implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy with a familiar deep learning API.
PPML @NeurIPS

MASEC @NeurIPS’23

Recent Publications

(A full list is available here: https://orcid.org/0000-0003-3178-9502)

More Publications

  • 2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments

    Details PDF

  • A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification

    Details PDF

  • A blockchain-orchestrated Federated Learning architecture for healthcare consortia

    Details PDF

  • A generic framework for privacy preserving deep learning

    Details PDF Code

  • ARIA: On the interaction between Architectures, Aggregation methods and Initializations in federated visual classification

    Details PDF

  • Adversarial interference and its mitigations in privacy-preserving collaborative machine learning

    Details PDF

  • Contribution Evaluation in Federated Learning: Examining Current Approaches

    Details PDF

  • Cooperative AI via Decentralized Commitment Devices

    Details PDF

  • End-to-end privacy preserving deep learning on multi-institutional medical imaging

    Details PDF

  • Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption

    Details PDF

Recent & Upcoming Talks

Teaching

Imperial College London (UK)

PhD student supervion

Labs and tutorials

  • ZKML: Introduction to Zero-Knowledge Proofs for Verifiable Machine Learning
  • Computer Graphics: OpenGL
  • Functional programming in Haskell: Introduction to functional programming
  • Java: Introduction to the language

Student projects supervised

ISIMA (Engineering School, France)

Courses led

  • Software Engineering 101: Git, Debugging with gdb
  • C++: Unit Testing (gtest), Template Meta-Programming, Standard Library
  • UML: Design Pattern, Class, State, Sequence Diagrams
  • Java: Java 7, Swing
  • Distributed computing: Cluster (PBS), Grid (EGI)
  • GPU Computing: CUDA, SIMD architecture

Student projects supervised

  • Various MEng projects supervised covering GPU parallelisation
  • Some associated publications (1, 2)

MSc in Computer Science (Blaise Pascal University, France)

Courses led

  • Introduction to parallel programming with CUDA

Contact