activity
20232026
most citedPrivacy-Preserving in Blockchain-based Federated Learning Systems

77 citations · 171 across the 24 of their papers we have counts for

collaborators
Showing 2025Show all

7 papers · 1 filter

cs.CR2025

SoK: The Last Line of Defense: On Backdoor Defense Evaluation

Gorka Abad, Marina Krček, Stefanos Koffas +7

Backdoor attacks pose a significant threat to deep learning models by implanting hidden vulnerabilities that can be activated by malicious inputs. While numerous defenses have been…

cs.CR2025

When Forgetting Triggers Backdoors: A Clean Unlearning Attack

Marco Arazzi, Antonino Nocera, Vinod P

Machine unlearning has emerged as a key component in ensuring ``Right to be Forgotten'', enabling the removal of specific data points from trained models. However, even when the un…

cs.CR2025★ 1 cited

XBreaking: Understanding how LLMs security alignment can be broken

Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera +1

Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adopt…

cs.CR2025

DroidTTP: Mapping Android Applications with TTP for Cyber Threat Intelligence

Dincy R Arikkat, Vinod P., Rafidha Rehiman K. A. +4

The widespread adoption of Android devices for sensitive operations like banking and communication has made them prime targets for cyber threats, particularly Advanced Persistent T…

cs.LG2025

Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings

Marco Arazzi, Mert Cihangiroglu, Antonino Nocera

Federated Averaging remains the most widely used aggregation strategy in federated learning due to its simplicity and scalability. However, its performance degrades significantly i…

cs.CR2025★ 1 cited

Secure Federated Data Distillation

Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo +1

Dataset Distillation (DD) is a powerful technique for reducing large datasets into compact, representative synthetic datasets, accelerating Machine Learning training. However, trad…

cs.IR2025★ 6 cited

Augmented Knowledge Graph Querying leveraging LLMs

Marco Arazzi, Davide Ligari, Serena Nicolazzo +1

Adopting Knowledge Graphs (KGs) as a structured, semantic-oriented, data representation model has significantly improved data integration, reasoning, and querying capabilities acro…