Publications de Apostolos Pyrgelis

Secure and federated genome-wide association studies for biobank-scale datasets

Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although crypt ...

NATURE PORTFOLIO2025

Scalable and Privacy-Preserving Federated Principal Component Analysis

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

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

IEEE COMPUTER SOC2023

slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference

Jean-Pierre Hubaux, Apostolos Pyrgelis, Sinem Sav, 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

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

Apostolos Pyrgelis, Francesco Intoci

Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., ne ...

IEEE2023

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

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

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 and Efficient Verification of the Outcome in Genome-Wide Association Studies

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Erman Ayday, Apostolos Pyrgelis

2022

Deploying Decentralized, Privacy-Preserving Proximity Tracing

Jean-Pierre Hubaux, James Richard Larus, Marcel Salathé, Mathias Josef Payer, Edouard Bugnion, Carmela González Troncoso, Theresa Stadler, Apostolos Pyrgelis, Sylvain Chatel, Wouter Lueks

2022

Every Byte Matters: Traffic Analysis of Bluetooth Wearable Devices

Jean-Pierre Hubaux, Apostolos Pyrgelis, Ludovic Barman

2021

POSEIDON: Privacy-Preserving Federated Neural Network Learning

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

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

2021