Research projects

AI research, Data & Privacy

Artificial Intelligence research and data privacy are at the heart of our team's values. Read our publications on AI, NLP and Data Privacy, in collaboration with ENS, INRIA, Imperial College London, McGill University and the OpenMined Foundation.

HDS Certification

Health Data Host (HDS) certification

ISO 27001 norm

ISO 27001




French authority standards compliance


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

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Nature Machine Intelligence, May 2021

Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, et al.

TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization

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EMNLP 2020

Clément Jumel, Annie Louis, Jackie C. K. Cheung

Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning

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ArXiv Preprint 2020

Alexandre Tamborrino, Nicola Pellicano, Baptiste Pannier, Pascal Voitot, Louise Naudin

Health research and innovation: Can we optimize the interface between startups/pharmaceutical companies and academic health care institutions or not?

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ArXiv Preprint 2020

Jean-François Dhainaut, Olivier Blin, ...,Corneliu Malciu

ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

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ArXiv Preprint 2020

Théo Ryffel, David Pointcheval, Francis Bach

Privacy-preserving medical image analysis

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Med-NeurIPS 2020

Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Da Lima Costa Junior, Jason Mancuso, Marcus Makowski, Daniel Rueckert, Rickmer Braren, Georgios Kaissis,

Toward trustworthy AI development: mechanisms for supporting verifiable claims

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ArXiv Preprint 2020

Miles Brundage, Shahar Avin, Jasmine Wang et al.

Partially Encrypted Machine Learning using Functional Encryption

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NeurIPS 2019

Théo Ryffel, Edouard Dufour-Sans, Romain Gay, Francis Bach, David Pointcheval

A Generic Framework for Privacy Preserving Deep Learning

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NeurIPS 2018 Workshop on Privacy-Preserving Machine Learning

Théo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, Jonathan Passerat-Palmbach

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Poster : on our publication "Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction" in association with Inria at the ACL Meeting (Canada)

Meetup FHIR France #8: OSIRIS on FHIR: How to model large sets of clinical and genomic data for multi-centric oncology research.

The Majors Originals #32: Corneliu Malciu from Arkhn.

Talk : on interactions between Differential Privacy and Multi-Party Computation at at ENS Paris (internal).

Presentation : of new tools for automated text analysis and structuration, at AP-HP.

Presentation : about data architectures in healthcare facilities and privacy enhancing techniques, at Roche.

Podcast : "Recruiting a technical team with expertise in data and healthcare". Listen to the podcast.

Talk : about building a multi-Purpose stack using FHIR as a persistence layer FHIR Dev Days 2020.

Talk : at the OpenMined Privacy Conference about concrete applications of privacy in healthcare.

Presentation : of privacy-preserving demos at Paris OpenMined Meetup.

Presentation : on Federated Analytics on Real-life Healthcare Data at the Federated Learning Conference.

Talk : at FHIR Dev Days: "Pyrog: an open-source mapping tool and ETL for FHIR".

Keynote : on Data Anonymization at the BNP Paribas - Plug And Play Deep Dive.

Presentation : "Tools for Safe AI" at Laboratoire de Sciences Cognitives et Psycholinguistiques (BabyCloud team).

Presentation : of Federated Learning Techniques at ENS Paris.

Talk : at Paris Meetup OpenMined on Secure & Federated Learning at Arkhn

Talk : at Paris Meetup OpenMined to present PySyft.

Blog Articles

PySyft + Opacus: Federated Learning with Differential Privacy

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By Théo Ryffel on september 30th, 2020

Encrypted inference with ResNet-18 using PyTorch + PySyft on ants & bees images

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by Théo Ryffel on September 15th, 2020

Anonymisation vs pseudonymisation: don't be fooled anymore!

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by Théo Ryffel on April 6th, 2020

Encrypted Deep Learning Training with Multi-Party Computation

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by Théo Ryffel on August 5th, 2019

Encrypted Deep Learning Classification with PyTorch & PySyft

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by Théo Ryffel on April 16th, 2019

Deep Learning & Federated Learning in 10 Lines of PyTorch + PySyft

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by Théo Ryffel on March 1st, 2019

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Ambitious projects need a community to support them, and they need to be accessible to as many people as possible.

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