Apostolos Pyrgelis

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Related publications (16)

System and method for privacy-preserving distributed training of machine learning 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 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, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis, Sylvain Chatel

2021

Privacy and Integrity Preserving Computations with CRISP

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

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

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

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