5 citations · 16 across the 9 of their papers we have counts for
4 papers · 2 filters
Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis
James Scheibner, Jean Louis Raisaro, Juan Ramón Troncoso-Pastoriza +4
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usa…
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…
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…