Arkhn | Nos travaux de recherche

Publications

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

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

EMNLP 2020

Clément Jumel, Annie Louis, Jackie C. K. Cheung
🧑💻 github.com/clementjumel/tesa

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

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?

ArXiv Preprint 2020

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

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

ArXiv Preprint 2020

Théo Ryffel, David Pointcheval, Francis Bach

Privacy-preserving medical image analysis

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,
🧑💻 github.com/OpenMined/PySyft

Toward trustworthy AI development: mechanisms for supporting verifiable claims

ArXiv Preprint 2020

Miles Brundage, Shahar Avin, Jasmine Wang et al.

Partially Encrypted Machine Learning using Functional Encryption

NeurIPS 2019

Théo Ryffel, Edouard Dufour-Sans, Romain Gay, Francis Bach, David Pointcheval
🧑💻 github.com/LaRiffle/collateral-learning

A Generic Framework for Privacy Preserving Deep Learning

NeurIPS 2018 Workshop on Privacy-Preserving Machine Learning

Théo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, Jonathan Passerat-Palmbach
🧑💻 github.com/OpenMined/PySyft

Articles de blog

PySyft + Opacus: Federated Learning with Differential Privacy

By Théo Ryffel on september 30th, 2020

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

by Théo Ryffel on September 15th, 2020

Anonymisation vs pseudonymisation: don't be fooled anymore!

by Théo Ryffel on April 6th, 2020

Encrypted Deep Learning Training with Multi-Party Computation

by Théo Ryffel on August 5th, 2019

Encrypted Deep Learning Classification with PyTorch & PySyft

by Théo Ryffel on April 16th, 2019

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

by Théo Ryffel on March 1st, 2019

Talks

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)

9 juillet 2023

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

26 avril2021

The Majors Originals #32: Corneliu Malciu from Arkhn.

29 mars 2021

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

18 mars 2021

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

16 mars2021

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

17 février2021

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

17 février 2021

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

20 novembre 2020

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

26 septembre 2020

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

8 juillet 2020

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

19 juin 2020

Poster : Poster presentation at NeurIPS 2019, Partially Encrypted Machine Learning using Functional Encryption (Canada).

10 décembre 2019

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

22 novembre 2019

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

15 octobre 2019

Talk : at School of Cybersecurity - Université Côte d'Azur on OpenMined.

19 juin 2019

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

6 juin 2019

Presentation : of Federated Learning Techniques at ENS Paris.

15 mai 2019

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

14 mai 2019

Talk : at Paris Meetup OpenMined to present PySyft.

20 mars 2019

Talk : at Spotlight session NeurIPS 2018 Workshop on Privacy-Preserving Machine Learning (Canada)

8 décembre 2018

Nos travaux de recherche

La recherche en IA, Data & Privacy

La recherche en intelligence artificielle et la protection des données sont au cœur des valeurs de notre équipe. Retrouvez nos publications en IA, NLP et Data Privacy, en collaboration notamment avec l'ENS, l'INRIA, l'Imperial College London, l'université McGill et la fondation OpenMined.

Publications

Articles de blog

Talks

Communauté Open-Source

Les projets ambitieux ont besoin d'une communauté pour les soutenir et ils doivent être accessibles au plus grand nombre