David Froelicher's Personal Page
David Froelicher's Personal Page
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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
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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
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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
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Slides
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Talk
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
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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
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