Retrieval-Augmented Generation (RAG) has attracted significant attention due to its ability to combine the generative capabilities of Large Language Models (LLMs) with knowledge obtained through efficient retrieval mechanisms over large-scale data collections. Currently, the majority of existing approaches overlook the risks associated with exposing sensitive or access-controlled information directly to the generation model. Only a few approaches propose techniques to instruct the generative model to refrain from disclosing sensitive information; however, recent studies have also demonstrated that such strategies remain vulnerable to prompt leaking attacks that can exfiltrate sensitive information via prompt injection. For these reasons, we propose a novel approach to Selective Disclosure in Retrieval-Augmented Generation, called SD-RAG, which decouples the enforcement of privacy constraints from the answer-generation process itself. SD-RAG relies on pre-redaction, applying sanitization and disclosure controls during the retrieval phase, prior to augmenting the question-answering LLM’s input with sensitive data. Moreover, we introduce a semantic mechanism to allow the ingestion of human-readable dynamic security and privacy constraints together with an optimized graph-based data model that supports fine-grained, policy-aware retrieval. In our experiments, we focus on the single-turn scenario, where an external attacker that relies on a malicious prompt template attempts to obtain sensitive information from the system by asking one question. Our experimental evaluation shows a promising improvement over the baseline in the single-turn prompt leaking scenario, achieving up to a 58% increase in the keyword-based privacy score metric that we introduce.

Federated Transfer Learning (FTL) is the most general variation of Federated Learning. According to this distributed paradigm, a feature learning pre-step is commonly carried out by only one party, typically the server, on publicly shared data. After that, the Federated Learning phase takes place to train a classifier collaboratively using the learned feature extractor. Each involved client contributes by locally training only the classification layers on a private training set. The peculiarity of an FTL scenario makes it hard to understand whether poisoning attacks can be developed to craft an effective backdoor. State-of-the-art attack strategies assume the possibility of shifting the model attention toward relevant features introduced by a forged trigger injected in the input data by some untrusted clients. Of course, this is not feasible in FTL, as the learned features are fixed once the server performs the pre-training step. Consequently, in this paper, we investigate this intriguing Federated Learning scenario to identify and exploit a vulnerability obtained by combining eXplainable AI (XAI) and dataset distillation. In particular, the proposed attack can be carried out by one of the clients during the Federated Learning phase of FTL by identifying the optimal local for the trigger through XAI and encapsulating compressed information of the backdoor class. Due to its behavior, we refer to our approach as a focused backdoor approach (FB-FTL for short) and test its performance by explicitly referencing an image classification scenario. With an average 80% attack success rate, obtained results show the effectiveness of our attack also against existing defenses for Federated Learning.

As Machine Learning (ML) continues to evolve, so does the sophistication of security threats targeting data privacy and model integrity. In response, Machine Unlearning (MU) has emerged as a promising paradigm that enables the selective removal of data influence from trained models. By supporting compliance with privacy regulations (such as the GDPR’s right to be forgotten) and facilitating model refinement, MU holds significant practical and legal value. Additionally, MU effective deployment introduces new security concerns. In real-world settings, malicious actors may exploit vulnerabilities in MU mechanisms, such as incomplete or inaccurate data removal, to infer deleted information, reintroduce adversarial behavior, or manipulate model updates. These risks highlight the urgency of understanding how classical ML threats relate to the design and operation of MU systems. However, despite its growing relevance, this intersection remains underexplored. In this article, we present a structured analysis of four major attack classes in ML (Backdoor Attacks, Membership Inference Attacks, Adversarial Attacks, and Inversion Attacks) and examine their implications for MU across multiple dimensions: (i) as direct threats targeting MU mechanisms, (ii) as challenges that MU can potentially mitigate, (iii) as evaluation metrics to measure the effectiveness and performance of MU techniques, and (iv) as verification factors to validate the success and completeness of the Unlearning process. We note that not all attacks exhibit all these perspectives simultaneously; their relevance varies depending on the attack characteristics and MU scenario. We also propose a novel classification that reflects how these attacks are typically employed in this context. Finally, we identify open challenges, including ethical considerations, and highlight promising directions for future research to advance secure and privacy-preserving Machine Unlearning.

Industry 5.0 envisions manufacturing systems that are human-centric, sustainable, and resilient. In this context, the Internet of Everything (IoE) enables integration of devices, people, and processes into a unified digital ecosystem. This paper presents a modular, semantically enriched framework that supports this transition by managing heterogeneous data sources—such as IoT sensors, wearable devices, and smart objects—through a layered architecture. The platform enables real-time data stream processing, semantic interoperability, and secure, context-aware access. Anomaly detection is enabled through a privacy-preserving mechanism based on behavioral fingerprinting and federated learning. The platform supports immersive human-machine interaction via gesture recognition, empowering workers to control and interact with industrial systems. Use cases demonstrate the system’s ability to support gesture-based control and intelligent monitoring, highlighting its potential to enhance adaptability, security, and worker empowerment in Industry 5.0 environments.

Industry 5.0 represents an emerging industrial paradigm that emphasizes seamless collaboration between human workers, collaborative robots (cobots), and smart objects. Its goal is to enable intelligent, adaptive manufacturing environments that not only boost operational efficiency and ensure regulatory compliance but also enhance workplace safety. In this context, we designed a complete framework based on a Reinforcement Learning (RL) strategy for intelligent and privacy-preserving task reallocation. Central to our vision is the prioritization of human well-being ensuring that both worker safety and privacy are protected, while the performance and reliability of machines and devices are optimized to support a truly human-centric manufacturing system. Our solution monitors workers’ physiological states and detects signs of fatigue, stress, or overload, ensuring that tasks can be dynamically reallocated to another worker or cobot to promote well-being without manual intervention. Moreover, by ensuring biometric data remains local to the worker’s device, the system respects data sovereignty and avoids unnecessary sharing of sensitive health information, guaranteeing compliance with regulations like GDPR. Our solution can adapt dynamically to the changing conditions and needs of human operators creating a privacy-preserving, safe, and efficient collaborative environment between people and machines. A comprehensive experimental analysis assesses the accuracy and performance of the proposed approach.

In the landscape of Industry 5.0, Internet of Everything (IoE) networks are emerging as crucial components for connecting diverse industrial sensors and devices, expanding beyond traditional IoT boundaries to integrate people, processes, and data. However, this increased connectivity raises significant security concerns, as the growing complexity of IoE environments introduces new attack vectors and privacy risks. Additionally, the integration of heterogeneous devices and data sources presents both technical and semantic interoperability challenges, requiring robust mechanisms for meaningful data interpretation and secure exchange. This paper, developed within the HOMEY project, presents an architecture for gathering and monitoring semantic data streams in IoE environments, addressing both interoperability and security challenges. Our approach leverages Knowledge Graphs to represent sensor metadata, locations, access rights, and operational contexts, enabling dynamic stream monitoring and data querying. An approach based on Federated Learning allows distributed behavioral fingerprinting of IoE devices, which is exploited on top of the platform to perform anomaly detection from real-time data streams. The approach enhances reliable, privacy-preserving anomaly detection, contributing to the security and resilience of next-generation industrial IoE ecosystems.

Human Activity Recognition (HAR) is becoming a key component in contemporary settings like Industry 5.0 and advanced smart home systems. In this study, we propose the use of a time-triggered, probabilistic Extended Finite-State Machine (EFSM) to build a modular Digital Twin (DT) of the system made by a moving person and the corresponding environment - including the sensors for their monitoring - to realistically reproduce the daily activities of the person and the signals generated by the sensors. The use of an EFSM allows to model the details of user’s behaviors and to easily address the trade-off between accuracy and complexity of the model. In particular, the probabilistic nature of the EFSM allows to introduce variability in the simulations while maintaining the model simple. Simulations performed using the DT generate accurate extended data that can be used to feed and train HAR algorithms, while the corresponding ground truth is used to label the data for the evaluation of the algorithms. The empirical analysis of the generated patterns shows that closely capture the behavior of the occupants in a simulated indoor environment. Moreover, a simple model based on a Long-Short Term Memory (LSTM) neural network was devised to show the usage of the synthetic dataset in the inference of a person’s position based on motion sensor signals.

Adopting Knowledge Graphs (KGs) as a structured, semantic-oriented, data representation model has significantly improved data integration, reasoning, and querying capabilities across different domains. This is especially true in modern scenarios such as Industry 5.0, where the integration of data from humans, smart devices, and production processes is crucial, not only for industrial innovation, but also for supporting the digital transition of government administrations and organizations. However, the management, retrieval, and visualization of data from a KG using formal query languages can be difficult for non-expert users due to their technical complexity, thus limiting their usage inside industrial environments. For this reason, we introduce SparqLLM, a framework that utilizes a Retrieval-Augmented Generation (RAG) solution, to enhance the querying of Knowledge Graphs (KGs). SparqLLM executes the Extract, Transform, and Load (ETL) pipeline to construct KGs from raw data. It also features a natural language interface powered by Large Language Models (LLMs) to enable automatic SPARQL query generation. By integrating template-based methods as retrieved-context for the LLM, SparqLLM enhances query reliability and reduces semantic errors, ensuring more accurate and efficient KG interactions. Moreover, to improve usability, the system incorporates a dynamic visualization dashboard that adapts to the structure of the retrieved data, presenting the query results in an intuitive format. Rigorous experimental evaluations demonstrate that SparqLLM achieves high query accuracy, improved robustness, and user-friendly interaction with KGs, establishing it as a scalable solution to access semantic data.

Dataset Distillation (DD) is a powerful technique for reducing large datasets into compact, representative synthetic datasets, accelerating Machine Learning training. However, traditional DD methods operate in a centralized manner, which poses significant privacy threats and reduces its applicability. To mitigate these risks, we propose a Secure Federated Data Distillation (SFDD) framework to decentralize the distillation process while preserving privacy. Unlike existing Federated Distillation techniques that focus on training global models with distilled knowledge, our approach aims to produce a distilled dataset without exposing local contributions. We leverage the gradient-matching-based distillation method, adapting it for a distributed setting where clients contribute to the distillation process without sharing raw data. The central aggregator iteratively refines a synthetic dataset by integrating client-side updates while ensuring data confidentiality. To make our approach resilient to inference attacks perpetrated by the server that could exploit gradient updates to reconstruct private data, we create an optimized Local Differential Privacy approach, called LDPO-RLD (Label Differential Privacy Obfuscation via Randomized Linear Dispersion). Furthermore, we assess the framework’s resilience against malicious clients executing backdoor attacks (such as Doorping) and demonstrate robustness under the assumption of a sufficient number of participating clients. Our experimental results demonstrate the effectiveness of SFDD and that the proposed defense concretely mitigates the identified vulnerabilities, with minimal impact on the performance of the distilled dataset. By addressing the interplay between privacy and federation in dataset distillation, this work advances the field of privacy-preserving Machine Learning making our SFDD framework a viable solution for sensitive data-sharing applications.

The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Organizations are inadvertently enlarging their vulnerability to cyber threats by integrating more interconnected devices into their operations, which makes these threats both more sophisticated and more common. Consequently, organizations have been compelled to seek innovative approaches to mitigate the menaces inherent in their infrastructure. In response, considerable research efforts have been directed towards creating effective solutions for sharing Cyber Threat Intelligence (CTI). Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to proprietary and confidential data leaks. To tackle this problem, we designed a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm Learning and Blockchain technologies to enable businesses to collaborate, preserving the privacy of their CTI data. Moreover, our approach provides a way to assess the data and model quality and the trustworthiness of all the participants leveraging some validators through Zero Knowledge Proofs. Extensive experimentation has confirmed the accuracy and performance of our framework. Furthermore, our detailed attack model analyzes its resistance to attacks that could impact data and model quality. • Definition of a Swarm Learning approach for collaborative CTI. • Definition of a Blockchain-based solution for privacy preservation in CTI sharing. • Secure CTI validation using a consensus mechanism and Zero-Knowledge Proof.

The widespread use of Android devices for sensitive operations has made them prime targets for sophisticated cyber threats, including Advanced Persistent Threats (APT). Traditional malware detection methods focus primarily on malware classification, often failing to reveal the Tactics, Techniques, and Procedures (TTPs) used by attackers. To address this issue, we propose DroidTTP, a novel system for mapping Android malware to attack behaviors. We curated a dataset linking Android applications to Tactics and Techniques and developed an automated mapping approach using the Problem Transformation Approach and Large Language Models (LLMs). Our pipeline includes dataset construction, feature selection, data augmentation, model training, and explainability via SHAP. Furthermore, we explored the use of LLMs for TTP prediction using both Retrieval Augmented Generation and fine-tuning strategies. The Label Powerset XGBoost model achieved the best performance, with Jaccard Similarity scores of 0.9893 for Tactic classification and 0.9753 for Technique classification. The fine-tuned LLaMa model also performed competitively, achieving 0.9583 for Tactics and 0.9348 for Techniques. Although XGBoost slightly outperformed LLMs, the narrow performance gap highlights the potential of LLM-based approaches for Tactic and Technique prediction.

Human-centric design, intelligence, and seamless interconnectivity are key pillars of the Industry 5.0. A critical challenge in these scenarios is the efficient retrieval of relevant, context-aware information for workers within Internet of Everything (IoE) networks. Traditional information retrieval techniques struggle with the heterogeneous, dynamic data generated in industrial settings. To address this, we define a context-aware data model for IoE scenarios, on top of which we propose RAG-IoE, a novel Retrieval-Augmented Generation (RAG) solution to enable adaptive, scalable, and context-based information retrieval from both structured and unstructured data sources. Our approach organizes IoE data within a semantic framework, integrating hybrid retrieval methods. It combines structured search on a Knowledge Graph with unstructured data retrieval using embeddings stored in a vector database, followed by LLM-driven reasoning to refine results. This architecture enhances decision-making, reduces cognitive overload, and ensures precise guidance for industrial operators. We validate the efficiency and effectiveness of RAG-IoE using a novel dataset through both a user study and quantitative analysis, demonstrating its potential to optimize human-machine collaboration in Industry 5.0 environments.

Modern Artificial Intelligence (AI) research is having a huge impact in many technological domains. As in many other research areas, the application of AI in smart factories has been a key factor in the contribution to the “smartness”. Every aspect of industrial automation has been affected by the introduction of AI: the usage of AI solutions allows to introduce advanced capabilities for optimizing processes, increasing efficiency, and reducing costs. This paper analyzes some relevant aspects of the application and the impact of AI solutions on the current scenario of smart factories and industrial automation. We identify a list of significant topics related to this domain, and we report the main aspects related to them. The dissertation includes an initial quantitative analysis of the relevance of these topics in the scientific publications, a detailed description of the characteristics of the topics, and a discussion of the related challenges.

Recently, the Industry 5.0 is gaining attention as a novel paradigm, defining the next concrete steps toward more and more intelligent, green-aware, and user-centric digital systems. In an era in which smart devices typically adopted in the industry domain are more and more sophisticated and autonomous, the Internet of Things and its evolution, known as the Internet of Everything (IoE, for short), involving also people, robots, processes, and data in the network, represent the main driver to allow industries to put the experiences and needs of human beings at the center of their ecosystems. However, due to the extreme heterogeneity of the involved entities, their intrinsic need and capability to cooperate, and the aim to adapt to a dynamic user-centric context, special attention is required for the integration and processing of the data produced by such an IoE. This is the objective of the present paper, in which we propose a novel semantic model that formalizes the fundamental actors, elements and information of an IoE, along with their relationships. In our design, we focus on state-of-the-art design principles, in particular reuse, and abstraction, to build “SemIoE,” a lightweight ontology inheriting and extending concepts from well-known and consolidated reference ontologies. The defined semantic layer represents a core data model that can be extended to embrace any modern industrial scenario. It represents the base of an IoE knowledge graph, on the top of which, as an additional contribution, we analyze and define some essential services for an IoE-based industry.

The pervasiveness and high number of Internet of Things (IoT) applications in people’s daily lives make this context a very critical attack surface for cyber threats. The high heterogeneity of involved entities, both in terms of hardware and software characteristics, does not allow the definition of uniform, global, and efficient security solutions. Therefore, researchers have started to investigate novel mechanisms, in which a super node (a gateway, a hub, or a router) analyzes the interactions of the target node with other peers in the network, to detect possible anomalies. The most recent of these strategies base such an analysis on the modeling of the fingerprint of a node behavior in an IoT; nevertheless, existing solutions do not cope with the fully distributed nature of the referring scenario. In this paper, we try to provide a contribution in this setting, by designing a novel and fully distributed trust model exploiting point-to-point devices’ behavioral fingerprints, a distributed consensus mechanism, and Blockchain technology. In our solution we tackle the non-trivial issue of equipping smart things with a secure mechanism to evaluate, also through their neighbors, the trustworthiness of an object in the network before interacting with it. Beyond the detailed description of our framework, we also illustrate the security model associated with it and the tests carried out to evaluate its correctness and performance.

Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction between involved entities. In addition, previous studies have investigated federated learning as the main solution to enable a native privacy-preserving mechanism for the construction of global GNN models without collecting sensitive data into a single computation unit. Still, privacy issues may arise as the analysis of local model updates produced by the federated clients can return information related to sensitive local data. For this reason, researchers proposed solutions that combine federated learning with Differential Privacy strategies and community-driven approaches, which involve combining data from neighbor clients to make the individual local updates less dependent on local sensitive data.

Social Networks represent one of the most important online sources to share content across a world-scale audience. In this context, predicting whether a post will have any impact in terms of engagement is of crucial importance to drive the profitable exploitation of these media. In the literature, several studies address this issue by leveraging direct features of the posts, typically related to the textual content and the user publishing it. In this paper, we argue that the rise of engagement is also related to another key component, which is the semantic connection among posts published by users in social media. Hence, we propose TweetGage, a Graph Neural Network solution to predict the user engagement based on a novel graph-based model that represents the relationships among posts. To validate our proposal, we focus on the Twitter platform and perform a thorough experimental campaign providing evidence of its quality.

With the growing diffusion of the Internet of Things (IoT) technology across most of the aspects of people daily lives, security concerns have become critical to ensure the exploitation of advantages introduced by this technology. This is even more true in the context of Industry 4.0, for which the IoT is becoming an important driver for automation. The detection of anomalies in IoT systems to ensure the capability of such systems to tolerate attacks to single devices is a crucial aspect. Behavioral fingerprinting is a recent and promising security solution in this context, which still requires research efforts to embrace new challenges in such a complex environment. Existing solutions focus mostly on modeling the behavior of IoT devices by analyzing the information extracted from the header of exchanged networking packets. However, in many application contexts, also attacks on the content of the packets can lead to disruptive results. Our proposal focus on these approaches by addressing a fully distributed scenario in which computation is directly handled by IoT devices, also through delegation, and describes a novel behavioral fingerprinting approach based on features suitably engineered from packet payloads. The effective-ness of our proposed method is assessed by both simulated and experimental results.

People's daily life is increasingly intertwined with smart devices, which are more and more used in dynamic contexts. Therefore, searching and exploiting the wealth of information produced by the Internet of Things (IoT) require novel models, including a representation of the actual context of use. The definition of context is inherently difficult, due to the variety of application scenarios and user needs. In this article, we propose a general model for devices' contexts representing context components at different resolutions (or levels of granularity). This enables the definition of a multiresolution context-based algorithm for querying the IoT, according to given preferences and contexts that can be tightened or relaxed depending on the given application goal. Experimental results show how the proposed approach outperforms traditional solutions by increasing the retrieval of relevant results while keeping precision under control.

In this paper we propose a method for Offline Handwritten Mathematical Expression recognition. The method is a fast and accurate thanks to its architecture, which include both a Convolutional Neural Network and a Recurrent Neural Network. The CNN extracts features from the image to recognize and its output is provided to the RNN which produces the mathematical expression encoded in the LATEX language. To process both sequential and non-sequential mathematical expressions we also included a deconvolutional module which, in a recursive way, segments the image for additional analysis trough a recursive process. The results obtained show a very high accuracy obtained on a large handwritten data set of 9100 samples of handwritten expressions.

Airbnb is one of the most successful examples of sharing economy marketplaces. With rapid and global market penetration, understanding its attractiveness and evolving growth opportunities is key to plan business decision making. There is an ongoing debate, for example, about whether Airbnb is a hospitality service that fosters social exchanges between hosts and guests, as the sharing economy manifesto originally stated, or whether it is (or is evolving into being) a purely business transaction platform, the way hotels have traditionally operated. To answer these questions, we propose a novel market analysis approach that exploits customers’ reviews. Key to the approach is a method that combines thematic analysis and machine learning to inductively develop a custom dictionary for guests’ reviews. Based on this dictionary, we then use quantitative linguistic analysis on a corpus of 3.2 million reviews collected in 6 different cities, and illustrate how to answer a variety of market research questions, at fine levels of temporal, thematic, user and spatial granularity, such as (i) how the business vs social dichotomy is evolving over the years, (ii) what exact words within such top-level categories are evolving, (iii) whether such trends vary across different user segments and (iv) in different neighbourhoods.

In this paper, we propose a novel localization service to monitor the position of residents in assisted living facilities. The service supports a configurable balancing between precision and privacy, in such a way that the right of the residents to move freely in the environment in which they live without being tracked is preserved. However, in case of need, they can always be quickly localized. To do this, we implement, on top of an RFID-based architecture, a probabilistic model guaranteeing that the probability of identifying a person in a given (sensitive) place is at most k-1, where k represents the required privacy level. This is obtained by ensuring that the EPC sent by RFID tags is not an identifier, but is equal to that of at least other k - 1 people, each afferent to a different reader. We show that our method reaches the goal, resisting also attacks aimed at breaking privacy on the basis of humans' movement models. Importantly, privacy is guaranteed against both misuse of the administrator and client-side eavesdropping attacks.

Blockchain technology allows the implementation of a public ledger securely recording transactions among peers without the need of trusted third parties. For both researchers and industry IoT appears a domain in which there would be extraordinary benefits if the features of Blockchain can be exploited. Indeed, the possibility that IoT devices participate in public shared transactions enables a lot of challenging applications. However, there are some aspects that may limit the use of Blockchain in IoT. These are mainly related to the low computational power and storage capabilities of IoT devices. In this paper, we propose an alternative way to implement a public ledger overcoming the above drawbacks, thus appearing more suitable to IoT applications. The proposed protocol leverages the popular social network Twitter and works by building a meshed chain of tweets to ensure transaction security. Importantly, Twitter does not play neither the role of trusted third party nor the role of ledger provider.

Fake identities and identity theft are issues whose relevance is increasing in the social network domain. This paper deals with this problem by proposing an innovative approach which combines a collaborative mechanism implementing a trust graph with keystroke-dynamic-recognition techniques to trust identities. The trust of each node is computed on the basis of neighborhood recognition and behavioral biometric support. The model leverages the word of mouth propagation and a settable degree of redundancy to obtain robustness. Experimental results show the benefit of the proposed solution even if attack nodes are present in the social network.

There are many application contexts in which guaranteeing authenticity and integrity of documents is essential.In these cases, the typical solution relies on digital signature, which is based on the use of a PKI infrastructure and suitable devices (smart card or token USB).For several reasons, including certificate and device cost, many countries, such as the United States, the European Union, India, Brazil and Australia, have introduced the possibility to use simple generic electronic signature, which is less secure but reduces the drawbacks of digital signature.In this paper, we propose a new type of electronic signature that is based on the use of social networks.We formalize the proposal in a generic scenario and then, show a possible implementation on Twitter.Our proposal is proved to be secure, cheap and simple to adopt.

Assortativity is the preference for a person to relate to others who are someway similar.This property has been widely studied in real-life social networks in the past and, more recently, great attention is devoted to study various forms of assortativity also in online social networks, being aware that it does not suffice to apply past scientific results obtained in the domain of real-life social networks.One of the aspects not yet analyzed in online social networks is interest assortativity, that is the preference for people to share the same interest (e.g., sport, music) with their friends.In this paper, we study this form of assortativity on Twitter, one of the most popular online social networks.After the introduction of the background theoretical model, we analyze Twitter, discovering that users clearly show interest assortativity.Beside the theoretical assessment, our result leads to identify a number of interesting possible applications.

Many real-life reputation models suffer from classical drawbacks making the systems where they are used vulnerable to users' misbehavior. TripAdvisor is a good example of this problem. Indeed, despite its popularity, the weakness of its reputation model is resulting in loss of credibility and growth of legal disputes. In this paper, we propose a reputation model abstractly considering service providers, users and feedbacks, and implementing the theoretical notion of certified reputation to concretely define a strategy to normalize feedback scores towards reliable values. We apply the model to the case of TripAdvisor, by proposing a solution to improve its dependability not increasing invasiveness nor reducing usability of the system. Moreover, it fully guarantees backward compatibility. In the context of project activities, we are in progress to fully implement the system and validate it on real-life data.

The social network phenomenon involves hundreds of millions of people every day. This enormous volume of activity results in a huge source of information that can be valuable in many fields, for both research and application purposes. The relevance of this information strongly depends on the evolution occurring in the social Web, in which interaction among different social networks and their cross-relationships are becoming progressively more important. This, in fact, represents the basis of an emergent scenario called Social Internetworking Scenario. However, efficiently accessing and fruitfully querying this huge information source is not easy, because no tool to support applications needing a massive utilization of cross-social-network data exists. In this paper, we fill this gap by proposing Social Network Account Knowledge Extractor (SNAKE), a system supporting the extraction of structural data from a social network account. SNAKE is implemented in such a way as to be easily integrated in any social-network-based application. To show the practical relevance of our proposal, we present our experience gained in three possible real-life applications strongly relying on information provided by SNAKE. Copyright © 2014 John Wiley & Sons, Ltd.

In new generation social networks, we expect that the paradigm of Social Internetworking Systems (SISs, for short) will be more and more important. In this new scenario, the role of Social Network Analysis is of course still crucial but the preliminary step to do is designing a good way to crawl the underlying graph. While this aspect has been deeply investigated in the field of social networks, it is an open issue when moving towards SISs. Indeed, we cannot expect that a crawling strategy which is good for social networks, is still valid in a Social Internetworking Scenario, due to its specific topological features. In this paper, we first confirm the above claim and, then, define a new crawling strategy specifically conceived for SISs. Finally, we show that it fully overcomes the drawbacks of the state-of-the-art crawling strategies.

The diffusion of social networks has stimulated folksonomy-based systems (hereafter, folk-systems) to equip themselves with functionalities for the management of social relationships among users. This suggests that folk-systems and social networks ha

Social internetworking systems are a significantly emerging new reality; they group together a set of social networks and allow their users to share resources, to acquire opinions and, more in general, to interact, even if these users belong to different social networks and, therefore, did not previously know each other. In this context the notions of trust and reputation play a very relevant role. These notions have been widely studied in the past in several contexts whereas they have been largely neglected in the social internetworking research; however, since this application field presents several peculiarities, the results found in other application contexts are not automatically valid here. This paper introduces a model to represent and handle trust and reputation in a social internetworking system and proposes an approach that exploits these parameters to provide users with suggestions about the most reliable persons they can contact or social networks they can register to.