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

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, 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

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

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

2022

Deploying Decentralized, Privacy-Preserving Proximity Tracing

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

2022

Every Byte Matters: Traffic Analysis of Bluetooth Wearable Devices

Jean-Pierre Hubaux, Ludovic Barman, Apostolos Pyrgelis

2021

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

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

Jean-Pierre Hubaux, Sylvain Chatel, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis

2021

Privacy and Integrity Preserving Computations with CRISP

Jean-Pierre Hubaux, Sylvain Chatel, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis

In the digital era, users share their personal data with service providers to obtain some utility, e.g., access to high-quality services. Yet, the induced information flows raise privacy and integrity concerns. Consequently, cautious users may want to prot ...

USENIX ASSOC2021

PriFi: Low-Latency Anonymity for Organizational Networks

Jean-Pierre Hubaux, Ludovic Barman, Ennan Zhai, Italo Ivan Dacosta Petrocelli, Bryan Alexander Ford, Mahdi Zamani, Apostolos Pyrgelis

Organizational networks are vulnerable to traffic-analysis attacks that enable adversaries to infer sensitive information from the network traffic – even if encryption is used. Typical anonymous communication networks are tailored to the Internet and are p ...

2020

Measuring Membership Privacy on Aggregate Location Time-Series

Carmela González Troncoso, Apostolos Pyrgelis

2020

Knock Knock, Who's There? Membership Inference on Aggregate Location Data

Carmela González Troncoso, Apostolos Pyrgelis

Aggregate location data is often used to support smart services and applications, e.g., generating live traffic maps or predicting visits to businesses. In this paper, we present the first study on the feasibility of membership inference attacks on aggrega ...

2018