Publications de Sinem Sav | EPFL Graph Search

Privacy-preserving federated neural network training and inference

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

EPFL2023

slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference

Jean-Pierre Hubaux, Sinem Sav, Apostolos Pyrgelis, Francesco Intoci

Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Prediction-as-a-Service (PaaS), where machine-learning predictions are computed ...

Springer Nature2023

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Sinem Sav, Apostolos Pyrgelis

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being commun ...

2022

Privacy-preserving federated neural network learning for disease-associated cell classification

Jean-Pierre Hubaux, Sinem Sav

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due t ...

ELSEVIER2022

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Sinem Sav, Apostolos Pyrgelis

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021

Scalable Privacy-Preserving Distributed Learning

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Sinem Sav, Apostolos Pyrgelis

2021

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

Jean-Pierre Hubaux, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Juan Ramón Troncoso-Pastoriza, Sinem Sav, Apostolos Pyrgelis

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupl ...

2021