7 citations · 29 across the 11 of their papers we have counts for
16 papers
SoK: Privacy-Preserving Collaborative Tree-based Model Learning
Sylvain Chatel, Apostolos Pyrgelis, Juan Ramon Troncoso-Pastoriza +1
Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal n…
Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies
Anisa Halimi, Leonard Dervishi, Erman Ayday +5
Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. Workflow systems model and record provenance describing the steps performed…
POSEIDON: Privacy-Preserving Federated Neural Network Learning
Sinem Sav, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza +4
In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an -party, federated learning setting. We propose a novel system, POSEI…
Privacy and Integrity Preserving Computations with CRISP
Sylvain Chatel, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza +1
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 p…
Decentralized Privacy-Preserving Proximity Tracing
Carmela Troncoso, Mathias Payer, Jean-Pierre Hubaux +31
This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system, referred to as DP3T, provides a technological foundat…
Scalable Privacy-Preserving Distributed Learning
David Froelicher, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis +4
In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstracti…