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4. Privacy-Preserving in Blockchain-based Federated Learning Systems

Author

M., Sameera K., Nicolazzo, Serena, Arazzi, Marco, Nocera, Antonino, A., Rafidha Rehiman K., P, Vinod, and Conti, Mauro

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution respect to the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning., Comment: 44 pages, 11 figures

Published
2024
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6. Alpha Phi-Shing Fraternity: Phishing Assessment in a Higher Education Institution

Author

Casagrande, Marco, Conti, Mauro, Fedeli, Monica, and Losiouk, Eleonora

Abstract

Phishing is a common social engineering attack aimed to steal personal information. Universities attract phishing attacks because: (1) they store employees and students sensitive data; (2) they save confidential documents; and (3) their infrastructures often lack security. In this paper, we showcase a phishing assessment at the University of Redacted aimed to identify the people, and the features of such people, that are more susceptible to phishing attacks. We delivered phishing emails to 1.508 subjects in three separate batches, collecting a clickrate equal to 30%, 11% and 13%, respectively. We considered several features (i.e., age, gender, role, working/studying field, email template) in univariate and multivariate analyses and found that students are more susceptible to phishing attacks than professors or technical/administrative staff, and that emails designed through a spearphishing approach receive a highest clickrate. We believe this work provides the foundations for setting up an effective educational campaign to prevent phishing attacks not only at the University of Redacted, but in any other university

Published
2023

10. Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence

Author

Tardelli, Serena, Nizzoli, Leonardo, Tesconi, Maurizio, Conti, Mauro, Nakov, Preslav, Martino, Giovanni Da San, and Cresci, Stefano

Subjects
Computer Science - Social and Information Networks, Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out the first dynamic analysis of coordinated behavior. To reach our goal we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. Thanks to our novel approach we find that: (i) coordinated communities feature variable degrees of temporal instability; (ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; (iii) some users exhibit distinct archetypal behaviors that have important practical implications; (iv) content and network characteristics contribute to explaining why users leave and join coordinated communities. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of coordinated communities, and on the patterns of online influence., Comment: Article published in PNAS 121 (20). Please, cite the published version

Published
2023
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