49 citations · 121 across the 8 of their papers we have counts for
14 papers
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption
George-Liviu Pereteanu, Amir Alansary, Jonathan Passerat-Palmbach
This work presents a novel protocol for fast secure inference of neural networks applied to computer vision applications. It focuses on improving the overall performance of the onl…
Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques
Ashly Lau, Jonathan Passerat-Palmbach
Differential privacy provides strong privacy guarantees for machine learning applications. Much recent work has been focused on developing differentially private models, however th…
Privacy-preserving medical image analysis
Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel +8
The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy pr…
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments
Harry Cai, Daniel Rueckert, Jonathan Passerat-Palmbach
Federated Learning harnesses data from multiple sources to build a single model. While the initial model might belong solely to the actor bringing it to the network for training, d…
A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification
Veneta Haralampieva, Daniel Rueckert, Jonathan Passerat-Palmbach
This work provides a comprehensive review of existing frameworks based on secure computing techniques in the context of private image classification. The in-depth analysis of these…
Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
Matei Grama, Maria Musat, Luis Muñoz-González +3
Federated learning (FL) has enabled training models collaboratively from multiple data owning parties without sharing their data. Given the privacy regulations of patient's healthc…