7 citations · 8 across the 5 of their papers we have counts for
7 papers
On the Robustness of Distributed Machine Learning against Transfer Attacks
Sébastien Andreina, Pascal Zimmer, Ghassan Karame
Although distributed machine learning (distributed ML) is gaining considerable attention in the community, prior works have independently looked at instances of distributed ML in e…
Defying the Odds: Solana's Unexpected Resilience in Spite of the Security Challenges Faced by Developers
Sébastien Andreina, Tobias Cloosters, Lucas Davi +5
Solana gained considerable attention as one of the most popular blockchain platforms for deploying decentralized applications. Compared to Ethereum, however, we observe a lack of r…
Larger-scale Nakamoto-style Blockchains Don't Necessarily Offer Better Security
Jannik Albrecht, Sebastien Andreina, Frederik Armknecht +3
Extensive research on Nakamoto-style consensus protocols has shown that network delays degrade the security of these protocols. Established results indicate that, perhaps surprisin…
Estimating Patch Propagation Times across (Blockchain) Forks
Sebastien Andreina, Lorenzo Alluminio, Giorgia Azzurra Marson +1
The wide success of Bitcoin has led to a huge surge of alternative cryptocurrencies (altcoins). Most altcoins essentially fork Bitcoin's code with minor modifications, such as the…
HCC: A Language-Independent Hardening Contract Compiler for Smart Contracts
Jens-Rene Giesen, Sebastien Andreina, Michael Rodler +2
Developing secure smart contracts remains a challenging task. Existing approaches are either impractical or leave the burden to developers for fixing bugs. In this paper, we propos…
MITOSIS: Practically Scaling Permissioned Blockchains
Giorgia Azzurra Marson, Sebastien Andreina, Lorenzo Alluminio +2
Scalability remains one of the biggest challenges to the adoption of permissioned blockchain technologies for large-scale deployments. Permissioned blockchains typically exhibit lo…
BaFFLe: Backdoor detection via Feedback-based Federated Learning
Sebastien Andreina, Giorgia Azzurra Marson, Helen Möllering +1
Recent studies have shown that federated learning (FL) is vulnerable to poisoning attacks that inject a backdoor into the global model. These attacks are effective even when perfor…