25 citations · 37 across the 19 of their papers we have counts for
8 papers · 1 filter
Leveraging the Verifier's Dilemma to Double Spend in Bitcoin
Tong Cao, Jérémie Decouchant, Jiangshan Yu
We describe and analyze perishing mining, a novel block-withholding mining strategy that lures profit-driven miners away from doing useful work on the public chain by releasing blo…
Aergia: Leveraging Heterogeneity in Federated Learning Systems
Bart Cox, Lydia Y. Chen, Jérémie Decouchant
Federated Learning (FL) is a popular approach for distributed deep learning that prevents the pooling of large amounts of data in a central server. FL relies on clients to update a…
Secure and Distributed Assessment of Privacy-Preserving Releases of GWAS
Túlio Pascoal, Jérémie Decouchant, Marcus Völp
Genome-wide association studies (GWAS) identify correlations between the genetic variants and an observable characteristic such as a disease. Previous works presented privacy-prese…
I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies
Túlio Pascoal, Jérémie Decouchant, Antoine Boutet +1
Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, ca…
Distributed Attestation Revocation in Self-Sovereign Identity
Rowdy Chotkan, Jérémie Decouchant, Johan Pouwelse
Self-Sovereign Identity (SSI) aspires to create a standardised identity layer for the Internet by placing citizens at the centre of their data, thereby weakening the grip of big te…
SyncPCN/PSyncPCN: Payment Channel Networks without Blockchain Synchrony
Oğuzhan Ersoy, Jérémie Decouchant, Satwik Prabhu Kimble +1
Payment channel networks (PCNs) enhance the scalability of blockchains by allowing parties to conduct transactions off-chain, i.e, without broadcasting every transaction to all blo…
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets
Federico Lucchetti, Jérémie Decouchant, Maria Fernandes +2
Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulat…
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Jin Xu, Chi Hong, Jiyue Huang +2
Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to…