activity
20182026
most citedCorrectness and Fairness of Tendermint-core Blockchains

21 citations · 29 across the 15 of their papers we have counts for

collaborators

17 papers

cs.CR2026

The Power of Backdoor Absorption in Community Training

Issam Seddik, Sami Souihi, Mohamed Tamaazousti +1

Backdoor attacks severely threaten large-scale AI models. When model owners delegate training to external compute providers within a decentralized training paradigm, adversaries ca…

cs.CR2025

PoTS: Proof-of-Training-Steps for Backdoor Detection in Large Language Models

Issam Seddik, Sami Souihi, Mohamed Tamaazousti +1

As Large Language Models (LLMs) gain traction across critical domains, ensuring secure and trustworthy training processes has become a major concern. Backdoor attacks, where malici…

cs.CR2025

Adversary-Augmented Simulation for Fairness Evaluation and Defense in Hyperledger Fabric

Erwan Mahe, Rouwaida Abdallah, Pierre-Yves Piriou +1

This paper presents an adversary model and a simulation framework specifically tailored for analyzing attacks on distributed systems composed of multiple distributed protocols, wit…

cs.CR2025

Order Fairness Evaluation of DAG-based ledgers

Erwan Mahe, Sara Tucci-Piergiovanni

Order fairness in distributed ledgers refers to properties that relate the order in which transactions are sent or received to the order in which they are eventually finalized, i.e…

cs.DC2024

Fractional Payment Transactions: Executing Payment Transactions in Parallel with Less than f+1 Validations

Rida Bazzi, Sara Tucci-Piergiovanni

We consider the problem of supporting payment transactions in an asynchronous system in which up to validators are subject to Byzantine failures under the control of an adaptiv…

cs.CR2024

Towards Secure and Trusted-by-Design Smart Contracts

Zaynah Dargaye, Önder Gürcan, Florent Kirchner +1

Distributed immutable ledgers, or blockchains, allow the secure digitization of evidential transactions without relying on a trusted third-party. Evidential transactions involve th…