Publications by Fiammetta Marulli
Published:
2026
- DetailsCampanile, L., de Biase, M. S., & Marulli, F. (2026). Design and evaluation of a privacy-preserving multi-level federated learning architecture for airport biometric check-in. Future Generation Computer Systems, 176, 108217. https://doi.org/https://doi.org/10.1016/j.future.2025.108217
Abstract
The rapid adoption of automated airport check-in systems using facial recognition raises significant privacy concerns due to their reliance on centralized deep learning models that store and transmit biometric data from edge devices. While Federated Learning (FL) is a promising approach for privacy preservation, its effectiveness in biometric identification remains underexplored, particularly in real-world environments like airports. This study assesses the privacy implications of FL in facial recognition by comparing three architectures. A first centralized system, where biometric data is sent to a central server for model training and inference, posing significant privacy risks. The second is a one-level FL architecture, where biometric data remains on local devices, and only model updates are shared with a central aggregator. The third is a two-level FL architecture, introducing an additional aggregation layer among airlines to enhance model generalization while preserving privacy. To ensure a rigorous privacy preservation evaluation, we integrate both quantitative and qualitative metrics. For the quantitative assessment, we leverage the Privacy Meter Tool, which enables simulations of Membership Inference Attacks and the application of Differential Privacy as a mitigation technique. For the qualitative evaluation, we conduct a Data Protection Impact Assessment to analyze potential privacy risks from a regulatory perspective. Additionally, we assess model accuracy, computational efficiency, and communication overhead to determine FL’s feasibility in large-scale airport environments. Our results show that while FL significantly reduces privacy risks, the two-level FL approach introduces new vulnerabilities, such as model poisoning risks and privacy-utility trade-offs, requiring further mitigation strategies like DP. - DetailsConference Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern ItalyCampanile, L., Di Bonito, L. P., Marulli, F., Balzanella, A., & Verde, R. (2026). Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern Italy [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 317–333. https://doi.org/10.1007/978-3-031-97645-2_21
Abstract
The increasing levels of CO2 and air pollutants represent a major challenge to environmental sustainability and public health, particularly in regions characterized by complex geographic and socio-economic dynamics. This work proposes a study focused on the Southern Italy regions, where environmental vulnerabilities are displayed, along with a limited availability of high-granularity data. The main aim of this work is to build and provide a comprehensive and detailed dataset tailored to the region’s unique needs, by leveraging datasets from EDGAR for greenhouse gases and air pollutants, integrated with demographic and territorial morphology data from ISTAT. The creation of composite indicators to monitor trends in emissions and pollution on a fine spatial scale is supported by the data set. These indicators enable initial insight into spatial disparities in pollutant concentrations, offering valuable data to inform targeted policy interventions. The work provided a foundation for next analytical studies, integrating different datasets and highlighting the potential for complex spatiotemporal analysis. The study provides a robust dataset and preliminary insights, enhancing the understanding of environmental dynamics in Southern Italy. Subsequent efforts will focus on extending this methodology to more extensive geographic contexts and incorporating real-time data for adaptive monitoring. The proposed framework also lays the groundwork for privacy-aware environmental monitoring solutions, enabling future integration with edge and IoT-based architectures while addressing privacy and data protection concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2025
- DetailsCampanile, L., de Biase, M. S., & Marulli, F. (2025). Edge-Cloud Distributed Approaches to Text Authorship Analysis: A Feasibility Study [Book chapter]. Lecture Notes on Data Engineering and Communications Technologies, 250, 284–293. https://doi.org/10.1007/978-3-031-87778-0_28
Abstract
Automatic authorship analysis, often referred to as stylometry, is a captivating yet contentious field that employs computational techniques to determine the authorship of textual artefacts. In recent years, the importance of author profiling has grown significantly due to the proliferation of automatic text generation systems. These include both early-generation bots and the latest generative AI-based models, which have heightened concerns about misinformation and content authenticity. This study proposes a novel approach to evaluate the feasibility and effectiveness of contemporary distributed learning methods. The approach leverages the computational advantages of distributed systems while preserving the privacy of human contributors, enabling the collection and analysis of extensive datasets of “human-written” texts in contrast to those generated by bots. More specifically, the proposed method adopts a Federated Learning (FL) framework, integrating readability and stylometric metrics to deliver a privacy-preserving solution for Authorship Attribution (AA). The primary objective is to enhance the accuracy of AA processes, thus achieving a more robust “authorial fingerprint”. Experimental results reveal that while FL effectively protects privacy and mitigates data exposure risks, the combined use of readability and stylometric features significantly increases the accuracy of AA. This approach demonstrates promise for secure and scalable AA applications, particularly in privacy-sensitive contexts and real-time edge computing scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. - DetailsMarulli, F., Campanile, L., Ragucci, G., Carbone, S., & Bifulco, M. (2025). Data Generation and Cybersecurity: A Major Opportunity or the Next Nightmare? [Conference paper]. Proceedings of the 2025 IEEE International Conference on Cyber Security and Resilience, CSR 2025, 969–974. https://doi.org/10.1109/CSR64739.2025.11130069
Abstract
In recent years, the proliferation of synthetic data generation techniques-driven by advances in artificial intelli-gence-has opened new possibilities across a wide range of fields, from healthcare to autonomous systems, by addressing critical data scarcity issues. However, this technological progress also brings with it a growing concern: the dual-use nature of synthetic data. While it offers powerful tools for innovation, it simultaneously introduces significant risks related to information disorder and cybersecurity. As AI systems become increasingly capable of producing highly realistic yet entirely fabricated content, the boundaries between authentic and artificial information blur, making it more difficult to detect manipulation, protect digital infrastructures, and maintain public trust. This work undertakes a preliminary exploration of the evolving nexus between Generative AI, Information Disorder, and Cybersecurity: it aims to investigate the complex interplay among these three and to map their dynamic interactions and reciprocal influences, highlighting both the potential benefits and the looming challenges posed by this evolving landscape. Moreover, it seeks to propose a conceptual framework for assessing these interdependencies through a set of indicative metrics, offering a foundation for future empirical evaluation and strategic response. © 2025 IEEE.
2024
- DetailsBarzegar, A., Campanile, L., Marrone, S., Marulli, F., Verde, L., & Mastroianni, M. (2024). Fuzzy-based Severity Evaluation in Privacy Problems: An Application to Healthcare [Conference paper]. Proceedings - 2024 19th European Dependable Computing Conference, EDCC 2024, 147–154. https://doi.org/10.1109/EDCC61798.2024.00037
Abstract
The growing diffusion of smart pervasive applications is starting to mine personal privacy: from Internet of Things to Machine Learning, the opportunities for privacy loss are many. As for other concerns involving people and goods as financial, safety and security, researchers and practitioners have defined in time different risk assessment procedures to have repeatable and accurate ways of detecting, quantifying and managing the (possible) source of privacy loss. This paper defines a methodology to deal with privacy risk assessment, overcoming the traditional dichotomy between qualitative (easy to apply) and quantitative (accurate) approaches. The present paper introduces an approach based on fuzzy logic, able to conjugate the benefits of both techniques. The feasibility of the proposed methodology is demonstrated using a healthcare case study. © 2024 IEEE. - DetailsBook Chapter Combining Federated and Ensemble Learning in Distributed and Cloud Environments: An Exploratory StudyMarulli, F., Campanile, L., Marrone, S., & Verde, L. (2024). Combining Federated and Ensemble Learning in Distributed and Cloud Environments: An Exploratory Study [Book chapter]. Lecture Notes on Data Engineering and Communications Technologies, 203, 297–306. https://doi.org/10.1007/978-3-031-57931-8_29
Abstract
Conventional modern Machine Learning (ML) applications involve training models in the cloud and then transferring them back to the edge, especially in an Internet of Things (IoT) enabled environment. However, privacy-related limitations on data transfer from the edge to the cloud raise challenges: among various solutions, Federated Learning (FL) could satisfy privacy related concerns and accommodate power and energy issues of edge devices. This paper proposes a novel approach that combines FL and Ensemble Learning (EL) to improve both security and privacy challenges. The presented methodology introduces an extra layer, the Federation Layer, to enhance security. It uses Bayesian Networks (BNs) to dynamically filter untrusted/unsecure federation clients. This approach presents a solution for increasing the security and robustness of FL systems, considering also privacy and performance aspects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. - DetailsMarulli, F., Campanile, L., de Biase, M. S., Marrone, S., Verde, L., & Bifulco, M. (2024). Understanding Readability of Large Language Models Output: An Empirical Analysis [Conference paper]. Procedia Computer Science, 246(C), 5273–5282. https://doi.org/10.1016/j.procs.2024.09.636
Abstract
Recently, Large Language Models (LLMs) have seen some impressive leaps, achieving the ability to accomplish several tasks, from text completion to powerful chatbots. The great variety of available LLMs and the fast pace of technological innovations in this field, is making LLM assessment a hard task to accomplish: understanding not only what such a kind of systems generate but also which is the quality of their results is of a paramount importance. Generally, the quality of a synthetically generated object could refer to the reliability of the content, to the lexical variety or coherence of the text. Regarding the quality of text generation, an aspect that up to now has not been adequately discussed is concerning the readability of textual artefacts. This work focuses on the latter aspect, proposing a set of experiments aiming to better understanding and evaluating the degree of readability of texts automatically generated by an LLM. The analysis is performed through an empirical study based on: considering a subset of five pre-trained LLMs; considering a pool of English text generation tasks, with increasing difficulty, assigned to each of the models; and, computing a set of the most popular readability indexes available from the computational linguistics literature. Readability indexes will be computed for each model to provide a first perspective of the readability of textual contents artificially generated can vary among different models and under different requirements of the users. The results obtained by evaluating and comparing different models provide interesting insights, especially into the responsible use of these tools by both beginners and not overly experienced practitioners. © 2024 The Authors. - DetailsVerde, L., Marulli, F., De Fazio, R., Campanile, L., & Marrone, S. (2024). HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis [Article]. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109021
Abstract
Background: Voice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. Method: In this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. Results: The reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. Conclusions: The comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders. © 2024 The Author(s) - DetailsCampanile, L., De Fazio, R., Di Giovanni, M., & Marulli, F. (2024). Beyond the Hype: Toward a Concrete Adoption of the Fair and Responsible Use of AI [Conference paper]. CEUR Workshop Proceedings, 3762, 60–65. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205601768&partnerID=40&md5=99140624de79e37b370ed4cf816c24e7
Abstract
Artificial Intelligence (AI) is a fast-changing technology that is having a profound impact on our society, from education to industry. Its applications cover a wide range of areas, such as medicine, military, engineering and research. The emergence of AI and Generative AI have significant potential to transform society, but they also raise concerns about transparency, privacy, ownership, fair use, reliability, and ethical considerations. The Generative AI adds complexity to the existing problems of AI due to its ability to create machine-generated data that is barely distinguishable from human-generated data. Bringing to the forefront the issue of responsible and fair use of AI. The security, safety and privacy implications are enormous, and the risks associated with inappropriate use of these technologies are real. Although some governments, such as the European Union and the United States, have begun to address the problem with recommendations and proposed regulations, it is probably not enough. Regulatory compliance should be seen as a starting point in a continuous process of improving the ethical procedures and privacy risk assessment of AI systems. The need to have a baseline to manage the process of creating an AI system even from an ethics and privacy perspective becomes progressively more important In this study, we discuss the ethical implications of these advances and propose a conceptual framework for the responsible, fair, and safe use of AI. © 2024 Copyright for this paper by its authors.
2023
- DetailsCampanile, L., de Fazio, R., Di Giovanni, M., Marrone, S., Marulli, F., & Verde, L. (2023). Inferring Emotional Models from Human-Machine Speech Interactions [Conference paper]. Procedia Computer Science, 225, 1241–1250. https://doi.org/10.1016/j.procs.2023.10.112
Abstract
Human-Machine Interfaces (HMIs) are getting more and more important in a hyper-connected society. Traditional HMIs are built considering cognitive features while emotional ones are often neglected, bringing sometimes such interfaces to misuse. As a part of a long run research, oriented to the definition of an HMI engineering approach, this paper concretely proposes a method to build an emotional-aware explicit model of the user starting from the behaviour of the human with a virtual agent. The paper also proposes an instance of this model inference process in voice assistants in an automatic depression context, which can constitute the core phase to realize a Human Digital Twin of a patient. The case study generated a model composed of Fluid Stochastic Petri Net sub-models, achieved after the data analysis by a Support Vector Machine. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) - DetailsMarrone, S., Campanile, L., De Fazio, R., Di Giovanni, M., Gentile, U., Marulli, F., & Verde, L. (2023). A Petri net oriented approach for advanced building energy management systems [Article]. Journal of Ambient Intelligence and Smart Environments, 15(3), 211–233. https://doi.org/10.3233/AIS-230065
Abstract
Sustainability is one of the main goals to pursue in several aspects of everyday life; the recent energy shortage and the price raise worsen this problem, especially in the management of energy in buildings. As the Internet of Things (IoT) is an assessed computing paradigm able to capture meaningful data from the field and send them to cloud infrastructures, other approaches are also enabled, namely model-based approaches. These methods can be used to predict functional and non-functional properties of Building Energy Management Systems (BEMS) before setting up them. This paper aims at bridging the gap between model-based approaches and physical realizations of sensing and small computing devices. Through an integrated approach, able to exploit the power of different dialects of Petri Nets, this paper proposes a methodology for the early evaluation of BEMS properties as well as the automatic generation of IoT controllers. © 2023 - IOS Press. All rights reserved. - DetailsBobbio, A., Campanile, L., Gribaudo, M., Iacono, M., Marulli, F., & Mastroianni, M. (2023). A cyber warfare perspective on risks related to health IoT devices and contact tracing [Article]. Neural Computing and Applications, 35(19), 13823–13837. https://doi.org/10.1007/s00521-021-06720-1
Abstract
The wide use of IT resources to assess and manage the recent COVID-19 pandemic allows to increase the effectiveness of the countermeasures and the pervasiveness of monitoring and prevention. Unfortunately, the literature reports that IoT devices, a widely adopted technology for these applications, are characterized by security vulnerabilities that are difficult to manage at the state level. Comparable problems exist for related technologies that leverage smartphones, such as contact tracing applications, and non-medical health monitoring devices. In analogous situations, these vulnerabilities may be exploited in the cyber domain to overload the crisis management systems with false alarms and to interfere with the interests of target countries, with consequences on their economy and their political equilibria. In this paper we analyze the potential threat to an example subsystem to show how these influences may impact it and evaluate a possible consequence. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. - DetailsDi Giovanni, M., Campanile, L., D’Onofrio, A., Marrone, S., Marulli, F., Romoli, M., Sabbarese, C., & Verde, L. (2023). Supporting the Development of Digital Twins in Nuclear Waste Monitoring Systems [Conference paper]. Procedia Computer Science, 225, 3133–3142. https://doi.org/10.1016/j.procs.2023.10.307
Abstract
In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) - DetailsConference Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins DesignCampanile, L., De Biase, M. S., De Fazio, R., Di Giovanni, M., Marulli, F., & Verde, L. (2023). Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins Design [Conference paper]. Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023, 301–306. https://doi.org/10.1109/CSR57506.2023.10224945
Abstract
Nowadays, the problem of system robustness, es-pecially in critical infrastructures, is a challenging open question. Some systems provide crucial services continuously failing, threatening the availability of the provided services. By designing a robust architecture, this criticality could be overcome or limited, ensuring service continuity. The definition of a resilient system involves not only its architecture but also the methodology implemented for the calculation and analysis of some indices, quantifying system performance. This study provides an innovative architecture for Digital Twins implementation based on a hybrid methodology for improving the control system in realtime. The introduced approach brings together different techniques. In particular, the work combines the point of strengths of Model-based methods and Data-driven ones, aiming to improve system performances. © 2023 IEEE.
2022
- DetailsCampanile, L., Iacono, M., Marulli, F., Gribaudo, M., & Mastroianni, M. (2022). A DSL-based modeling approach for energy harvesting IoT/WSN [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2022-May, 317–323. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130645195&partnerID=40&md5=f2d475b445f76d3b5f49752171c0fada
Abstract
The diffusion of intelligent services and the push for the integration of computing systems and services in the environment in which they operate require a constant sensing activity and the acquisition of different information from the environment and the users. Health monitoring, domotics, Industry 4.0 and environmental challenges leverage the availability of cost-effective sensing solutions that allow both the creation of knowledge bases and the automatic process of them, be it with algorithmic approaches or artificial intelligence solutions. The foundation of these solutions is given by the Internet of Things (IoT), and the substanding Wireless Sensor Networks (WSN) technology stack. Of course, design approaches are needed that enable defining efficient and effective sensing infrastructures, including energy related aspects. In this paper we present a Domain Specific Language for the design of energy aware WSN IoT solutions, that allows domain experts to define sensor network models that may be then analyzed by simulation-based or analytic techniques to evaluate the effect of task allocation and offioading and energy harvesting and utilization in the network. The language has been designed to leverage the SIMTHESys modeling framework and its multiformalism modeling evaluation features. ©ECMS Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat (Editors) 2022 - DetailsVerde, L., Campanile, L., Marulli, F., & Marrone, S. (2022). Speech-based Evaluation of Emotions-Depression Correlation. Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022. https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927758
Abstract
Early detection of depression symptoms is fundamental to limit the onset of further associated behavioural disorders, such as psychomotor or social withdrawal. The combination of Artificial Intelligence and speech analysis revealed the existence of objectively measurable physical manifestations for early detection of depressive symptoms, constituting a valid support to evaluate these signals. To push forward the research state-of-art, this aim of this paper is to understand quantitative correlations between emotional states and depression by proposing a study across different datasets containing speech of both depressed/non-depressed people and emotional-related samples. The relationship between affective measures and depression can, in fact, a support to evaluate the presence of depression state. This work constitutes a preliminary step of a study whose final aim is to pursue AI-powered personalized medicine by building sophisticated Clinical Decision Support Systems for depression, as well as other psychological disorders. © 2022 IEEE. - DetailsCampanile, L., Marrone, S., Marulli, F., & Verde, L. (2022). Challenges and Trends in Federated Learning for Well-being and Healthcare [Conference paper]. Procedia Computer Science, 207, 1144–1153. https://doi.org/10.1016/j.procs.2022.09.170
Abstract
Currently, research in Artificial Intelligence, both in Machine Learning and Deep Learning, paves the way for promising innovations in several areas. In healthcare, especially, where large amounts of quantitative and qualitative data are transferred to support studies and early diagnosis and monitoring of any diseases, potential security and privacy issues cannot be underestimated. Federated learning is an approach where privacy issues related to sensitive data management can be significantly reduced, due to the possibility to train algorithms without exchanging data. The main idea behind this approach is that learning models can be trained in a distributed way, where multiple devices or servers with decentralized data samples can provide their contributions without having to exchange their local data. Recent studies provided evidence that prototypes trained by adopting Federated Learning strategies are able to achieve reliable performance, thus by generating robust models without sharing data and, consequently, limiting the impact on security and privacy. This work propose a literature overview of Federated Learning approaches and systems, focusing on its application for healthcare. The main challenges, implications, issues and potentials of this approach in the healthcare are outlined. © 2022 The Authors. Published by Elsevier B.V. - DetailsConference Sensitive Information Detection Adopting Named Entity Recognition: A Proposed MethodologyCampanile, L., de Biase, M. S., Marrone, S., Marulli, F., Raimondo, M., & Verde, L. (2022). Sensitive Information Detection Adopting Named Entity Recognition: A Proposed Methodology [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13380 LNCS, 377–388. https://doi.org/10.1007/978-3-031-10542-5_26
Abstract
Protecting and safeguarding privacy has become increasingly important, especially in recent years. The increasing possibilities of acquiring and sharing personal information and data through digital devices and platforms, such as apps or social networks, have increased the risks of privacy breaches. In order to effectively respect and guarantee the privacy and protection of sensitive information, it is necessary to develop mechanisms capable of providing such guarantees automatically and reliably. In this paper we propose a methodology able to automatically recognize sensitive data. A Named Entity Recognition was used to identify appropriate entities. An improvement in the recognition of these entities is achieved by evaluating the words contained in an appropriate context window by assessing their similarity to words in a domain taxonomy. This, in fact, makes it possible to refine the labels of the recognized categories using a generic Named Entity Recognition. A preliminary evaluation of the reliability of the proposed approach was performed. In detail, texts of juridical documents written in Italian were analyzed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. - DetailsConference A Federated Consensus-Based Model for Enhancing Fake News and Misleading Information DebunkingMarulli, F., Verde, L., Marrore, S., & Campanile, L. (2022). A Federated Consensus-Based Model for Enhancing Fake News and Misleading Information Debunking [Conference paper]. Smart Innovation, Systems and Technologies, 309, 587–596. https://doi.org/10.1007/978-981-19-3444-5_50
Abstract
Misinformation and Fake News are hard to dislodge. According to experts on this phenomenon, to fight disinformation a less credulous public is needed; so, current AI techniques can support misleading information debunking, given the human tendency to believe “facts” that confirm biases. Much effort has been recently spent by the research community on this plague: several AI-based approaches for automatic detection and classification of Fake News have been proposed; unfortunately, Fake News producers have refined their ability in eluding automatic ML and DL-based detection systems. So, debunking false news represents an effective weapon to contrast the users’ reliance on false information. In this work, we propose a preliminary study aiming to approach the design of effective fake news debunking systems, harnessing two complementary federated approaches. We propose, firstly, a federation of independent classification systems to accomplish a debunking process, by applying a distributed consensus mechanism. Secondly, a federated learning task, involving several cooperating nodes, is accomplished, to obtain a unique merged model, including features of single participants models, trained on different and independent data fragments. This study is a preliminary work aiming to to point out the feasibility and the comparability of these proposed approaches, thus paving the way to an experimental campaign that will be performed on effective real data, thus providing an evidence for an effective and feasible model for detecting potential heterogeneous fake news. Debunking misleading information is mission critical to increase the awareness of facts on the part of news consumers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
2021
- DetailsCampanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Designing a GDPR compliant blockchain-based IoV distributed information tracking system [Article]. Information Processing and Management, 58(3). https://doi.org/10.1016/j.ipm.2021.102511
Abstract
Blockchain technologies and distributed ledgers enable the design and implementation of trustable data logging systems that can be used by multiple parties to produce a non-repudiable database. The case of Internet of Vehicles may greatly benefit of such a possibility to track the chain of responsibility in case of accidents or damages due to bad or omitted maintenance, improving the safety of circulation and helping granting a correct handling of related legal issues. However, there are privacy issues that have to be considered, as tracked information potentially include data about private persons (position, personal habits), commercially relevant information (state of the fleet of a company, freight movement and related planning, logistic strategies), or even more critical knowledge (e.g., considering vehicles belonging to police, public authorities, governments or officers in sensible positions). In the European Union, all this information is covered by the General Data Protection Regulation (GDPR). In this paper we propose a reference model for a system that manages relevant information to show how blockchain can support GDPR compliant solutions for Internet of Vehicles, taking as a reference an integrated scenario based on Italy, and analyze a subset of its use cases to show its viability with reference to privacy issues. © 2021 Elsevier Ltd - DetailsCampanile, L., Cantiello, P., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12956 LNCS, 3–14. https://doi.org/10.1007/978-3-030-87010-2_1
Abstract
Blockchains provide a valid and profitable support for the implementation of trustable and secure distributed ledgers, in support to groups of subjects that are potentially competitors in conflict of interest but need to share progressive information recording processes. Blockchains prevent data stored in blocks from being altered or deleted, but there are situations in which stored information must be deleted or made inaccessible on request or periodically, such as the ones in which GDPR is applicable. In this paper we present literature solutions and design an implementation in the context of a traffic management system for the Internet of Vehicles based on the Pseudonymization/Cryptography solution, evaluating its viability, its GDPR compliance and its level of risk. © 2021, Springer Nature Switzerland AG. - DetailsJournal Privacy regulations, smart roads, blockchain, and liability insurance: Putting technologies to workCampanile, L., Iacono, M., Levis, A. H., Marulli, F., & Mastroianni, M. (2021). Privacy regulations, smart roads, blockchain, and liability insurance: Putting technologies to work [Article]. IEEE Security and Privacy, 19(1), 34–43. https://doi.org/10.1109/MSEC.2020.3012059
Abstract
Smart streets promise widely available traffic information to help improve people’s safety. Unfortunately, gathering that data may threaten privacy. We describe an architecture that exploits a blockchain and the Internet of Vehicles and show its compliance with the General Data Protection Regulation. © 2003-2012 IEEE. - DetailsCampanile, L., Forgione, F., Marulli, F., Palmiero, G., & Sanghez, C. (2021). Dataset Anonimyzation for Machine Learning: An ISP Case Study [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12950 LNCS, 589–597. https://doi.org/10.1007/978-3-030-86960-1_42
Abstract
Internet Service Providers technical support needs personal data to predict potential anomalies. In this paper, we performed a comparative study of forecasting performance using raw data and anonymized data, in order to assess how much performance may vary, when plain personal data are replaced by anonymized personal data. © 2021, Springer Nature Switzerland AG. - DetailsCampanile, L., Cantiello, P., Iacono, M., Lotito, R., Marulli, F., & Mastroianni, M. (2021). Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 354–363. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135227609&partnerID=40&md5=5a7c117fa01d0ba8d779b0e092bc0f63
Abstract
Local pollution is a problem that affects urban areas and has effects on the quality of life and on health conditions. In order to not develop strict measures and to better manage territories, the national authorities have applied a vast range of predictive models. Actually, the application of machine learning has been studied in the last decades in various cases with various declination to simplify this problem. In this paper, we apply a regression-based analysis technique to a dataset containing official historical local pollution and weather data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome by the use of a maximum inclusion strategy as performing the joining action with”outer” mode. Among the four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R2 (Coefficient of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean Squared Error), equal to 0.12 and 0.04 respectively. © 2021 by SCITEPRESS - Science and Technology Publications, Lda. - DetailsMarulli, F., Verde, L., & Campanile, L. (2021). Exploring data and model poisoning attacks to deep learning-based NLP systems [Conference paper]. Procedia Computer Science, 192, 3570–3579. https://doi.org/10.1016/j.procs.2021.09.130
Abstract
Natural Language Processing (NLP) is being recently explored also to its application in supporting malicious activities and objects detection. Furthermore, NLP and Deep Learning have become targets of malicious attacks too. Very recent researches evidenced that adversarial attacks are able to affect also NLP tasks, in addition to the more popular adversarial attacks on deep learning systems for image processing tasks. More precisely, while small perturbations applied to the data set adopted for training typical NLP tasks (e.g., Part-of-Speech Tagging, Named Entity Recognition, etc..) could be easily recognized, models poisoning, performed by the means of altered data models, typically provided in the transfer learning phase to a deep neural networks (e.g., poisoning attacks by word embeddings), are harder to be detected. In this work, we preliminary explore the effectiveness of a poisoned word embeddings attack aimed at a deep neural network trained to accomplish a Named Entity Recognition (NER) task. By adopting the NER case study, we aimed to analyze the severity of such a kind of attack to accuracy in recognizing the right classes for the given entities. Finally, this study represents a preliminary step to assess the impact and the vulnerabilities of some NLP systems we adopt in our research activities, and further investigating some potential mitigation strategies, in order to make these systems more resilient to data and models poisoning attacks. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International. - DetailsCampanile, L., Marulli, F., Mastroianni, M., Palmiero, G., & Sanghez, C. (2021). Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 343–353. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137959400&partnerID=40&md5=eb78330cb4d585e500b77cd906edfbc7
Abstract
In this paper, we introduce a solution aiming to improve the accuracy of the surface temperature detection in an outdoor environment. The temperature sensing subsystem relies on Mobotix thermal camera without the black body, the automatic compensation subsystem relies on Raspberry Pi with Node-RED and TensorFlow 2.x. The final results showed that it is possible to automatically calibrate the camera using machine learning and that it is possible to use thermal imaging cameras even in critical conditions such as outdoors. Future development is to improve performance using computer vision techniques to rule out irrelevant measurements. © 2021 by SCITEPRESS - Science and Technology Publications, Lda. - DetailsMarulli, F., Balzanella, A., Campanile, L., Iacono, M., & Mastroianni, M. (2021). Exploring a Federated Learning Approach to Enhance Authorship Attribution of Misleading Information from Heterogeneous Sources [Conference paper]. Proceedings of the International Joint Conference on Neural Networks, 2021-July. https://doi.org/10.1109/IJCNN52387.2021.9534377
Abstract
Authorship Attribution (AA) is currently applied in several applications, among which fraud detection and anti-plagiarism checks: this task can leverage stylometry and Natural Language Processing techniques. In this work, we explored some strategies to enhance the performance of an AA task for the automatic detection of false and misleading information (e.g., fake news). We set up a text classification model for AA based on stylometry exploiting recurrent deep neural networks and implemented two learning tasks trained on the same collection of fake and real news, comparing their performances: one is based on Federated Learning architecture, the other on a centralized architecture. The goal was to discriminate potential fake information from true ones when the fake news comes from heterogeneous sources, with different styles. Preliminary experiments show that a distributed approach significantly improves recall with respect to the centralized model. As expected, precision was lower in the distributed model. This aspect, coupled with the statistical heterogeneity of data, represents some open issues that will be further investigated in future work. © 2021 IEEE.
2020
- DetailsConference Privacy regulations challenges on data-centric and iot systems: A case study for smart vehiclesCampanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2020). Privacy regulations challenges on data-centric and iot systems: A case study for smart vehicles [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 507–520. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089476036&partnerID=40&md5=c18dd73c221ec312a330521bf03d332e
Abstract
Internet of Things (IoTs) services and data-centric systems allow smart and efficient information exchanging. Anyway, even if existing IoTs and cyber security architectures are enforcing, they are still vulnerable to security issues, as unauthorized access, data breaches, intrusions. They can’t provide yet sufficiently robust and secure solutions to be applied in a straightforward way, both for ensuring privacy preservation and trustworthiness of transmitted data, evenly preventing from its fraudulent and unauthorized usage. Such data potentially include critical information about persons’ privacy (locations, visited places, behaviors, goods, anagraphic data and health conditions). So, novel approaches for IoTs and data-centric security are needed. In this work, we address IoTs systems security problem focusing on the privacy preserving issue. Indeed, after the European Union introduced the General Data Protection Regulation (GDPR), privacy data protection is a mandatory requirement for systems producing and managing sensible users’ data. Starting from a case study for the Internet of Vehicles (IoVs), we performed a pilot study and DPIA assessment to analyze possible mitigation strategies for improving the compliance of IoTs based systems to GDPR requirements. Our preliminary results evidenced that the introduction of blockchains in IoTs systems architectures can improve significantly the compliance to privacy regulations. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. - DetailsCampanile, L., Iacono, M., Marulli, F., Mastroianni, M., & Mazzocca, N. (2020). Toward a fuzzy-based approach for computational load offloading of IoT devices [Article]. Journal of Universal Computer Science, 26(11), 1455–1474. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100774625&partnerID=40&md5=1d88148124172a6b7ac374387f21199a
Abstract
Technological development and market expansion offer an increased availability of resources and computing power on IoT nodes at affordable cost. The edge computing paradigm allows keeping locally on the edge of the network a part of computing, while keeping all advantages of the cloud and adding support for privacy, real-time and network resilience. This can be further improved in IoT applications by flexibly harvesting resources on IoT nodes, by moving part of the computing tasks related to data from the edge server to the nodes, raising the abstraction level of the data aspects of the architecture and potentially enabling larger IoT networks to be efficiently deployed and managed, in a stand-alone logic or as a component of edge architecture. Anyway, an efficient energy management mechanism is needed for battery powered IoT networks, the most flexible implementations, that dynamically balances task allocation and execution in order to In this paper we present a fuzzy logic based power management strategy for IoT subsystem that aims at maximizing the duration of the network by locally migrating part of the computing tasks between nodes. As our goal is to enable the deployment of semi-autonomic large IoT networks, our proposal does not rely on external resources for migration control and operates on a local basis to ensure scalability: at the best of our knowledge, this diferentiates our proposal with respect to similar solutions available in literature. © 2020, IICM. All rights reserved. - DetailsMainenti, G., Campanile, L., Marulli, F., Ricciardi, C., & Valente, A. S. (2020). Machine learning approaches for diabetes classification: Perspectives to artificial intelligence methods updating [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 533–540. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089519717&partnerID=40&md5=bf7cc36e86c1988dd85e04c2fce06de1
Abstract
In recent years the application of Machine Learning (ML) and Artificial Intelligence (AI) techniques in healthcare helped clinicians to improve the management of chronic patients. Diabetes is among the most common chronic illness in the world for which often is still challenging do an early detection and a correct classification of type of diabetes to an individual. In fact it often depends on the circumstances present at the time of diagnosis, and many diabetic individuals do not easily fit into a single class. The aim is this paper is the application of ML techniques in order to classify the occurrence of different mellitus diabetes on the base of clinical data obtained from diabetic patients during the daily hospitals activities. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. - DetailsCampanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2020). A simulation study on a WSN for emergency management [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 34(1), 384–392. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094937629&partnerID=40&md5=69ee7b771d76c72bd5012883b86e67ca
Abstract
Wireless Sensors Networks (WSN) are one of the ways to provide the communication infrastructure for advanced applications based on the Internet of Things (IoT) paradigm. IoT supports high level applications over WSN to provide services in a number of fields. WSN are also suitable to support critical applications, as the supporting technologies are consolidated and standard network services can be used on top of the specific layers. Furthermore, generic distributed or network-enabled software can be run over the nodes of a WSN. In this paper we evaluate and compare performances of IEEE 802.llg and 802.1 In, two implementations of the popular Wi-Fi technology, to support the deployment and utilization of an energy management support system, used to monitor the field by a team of firefighters during a mission. Evaluation on an example scenario is done by using ns-3, an open network simulator characterized by its realistic details, to understand the actual limitations of the two standards besides theoretical limits. © ECMS Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther. - DetailsCampanile, L., Iacono, M., Martinelli, F., Marulli, F., Mastroianni, M., Mercaldo, F., & Santone, A. (2020). Towards the Use of Generative Adversarial Neural Networks to Attack Online Resources [Conference paper]. Advances in Intelligent Systems and Computing, 1150 AISC, 890–901. https://doi.org/10.1007/978-3-030-44038-1_81
Abstract
The role of remote resources, such as the ones provided by Cloud infrastructures, is of paramount importance for the implementation of cost effective, yet reliable software systems to provide services to third parties. Cost effectiveness is a direct consequence of a correct estimation of resource usage, to be able to define a budget and estimate the right price to put own services on the market. Attacks that overload resources with non legitimate requests, being them explicit attacks or just malicious, non harmful resource engagements, may push the use of Cloud resources beyond estimation, causing additional costs, or unexpected energy usage, or a lower overall quality of services, so intrusion detection devices or firewalls are set to avoid undesired accesses. We propose the use of Generative Adversarial Neural Networks (GANs) to setup a method for shaping request based attacks capable of reaching resources beyond defenses. The approach is studied by using a publicly available traffic data set, to test the concept and demonstrate its potential applications. © 2020, Springer Nature Switzerland AG. - DetailsCampanile, L., Gribaudo, M., Iacono, M., Marulli, F., & Mastroianni, M. (2020). Computer network simulation with ns-3: A systematic literature review [Article]. Electronics (Switzerland), 9(2). https://doi.org/10.3390/electronics9020272
Abstract
Complexity of current computer networks, including e.g., local networks, large structured networks, wireless sensor networks, datacenter backbones, requires a thorough study to perform analysis and support design. Simulation is a tool of paramount importance to encompass all the different aspects that contribute to design quality and network performance (including as well energy issues, security management overheads, dependability), due to the fact that such complexity produces several interactions at all network layers that is not easily modellable with analytic approaches. In this systematic literature review we aim to analyze, basing our investigation on available literature, the adoption of a popular network simulator, namely ns-3, and its use in the scientific community. More in detail, we are interested in understanding what are the impacted application domains in which authors prefer ns-3 to other similar tools and how extensible it is in practice according to the experience of authors. The results of our analysis, which has been conducted by especially focusing on 128 papers published between 2009 to 2019, reveals that 10% of the evaluated papers were discarded because they represented informal literature; most of the studies presented comparisons among different network simulators, beyond ns-3 and conceptual studies related to performance assessment and validation and routing protocols. Only about 30% of considered studies present extensions of ns-3 in terms of new modules and only about 10% present effective case studies demonstrating the effectiveness of employing network simulator in real application, except conceptual and modeling studies. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.