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Scalable and Privacy-Preserving Federated Principal Component Analysis

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the …

David Froelicher, H. Cho, M. Edupalli, J. S. Sousa, J.-P. Bossuat, A. Pyrgelis, J. R. Troncoso-Pastoriza, J.-P. Hubaux, B. Berger

POSEIDON: Privacy-Preserving Federated Neural Network Learning

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated …

S. Sav, A. Pyrgelis, J. R. Troncoso-Pastoriza, David Froelicher, J.-P. Bossuat, J. S. Sousa, J.-P. Hubaux

Scalable Privacy-Preserving Distributed Learning

In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by …

David Froelicher, J. R. Troncoso-Pastoriza, A. Pyrgelis, S. Sav, J. S. Sousa, J.-P. Bossuat, J.-P. Hubaux

UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be …

David Froelicher, P. Egger, J. S. Sousa, J. L. Raisaro, Z. Huang, C. Mouchet, B. Ford, J.-P. Hubaux

SPORT: Sharing Proofs of Retrievability across Tenants

Proofs of Retrievability (POR) are cryptographic proofs which provide assurance to a single tenant (who creates tags using his secret …

F. Armknecht, J.-M. Bohli, G. Karame, David Froelicher