1 citations · 1 across the 2 of their papers we have counts for
5 papers
CAPMAS: Capability-Based Delegation of Privileges in Multi-Agent Systems
Rasmus Moorits Veski, Rachid Guerraoui, David Froelicher
Agentic systems require secure and efficient delegation of privileges across multiple collaborating agents. Existing approaches fall into two categories. Some propagate user identi…
Scalable and Privacy-Preserving Federated Principal Component Analysis
David Froelicher, Hyunghoon Cho, Manaswitha Edupalli +6
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 priv…
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…
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…
Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets
David Froelicher, Juan R. Troncoso-Pastoriza, Joao Sa Sousa +1
Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensiti…