In this paper, we present a framework that integrates AI-based derivation of Access and Usage Control policies for IoT devices, using Large Language Models (LLMs) to automate the generation of policies from unstructured natural language commands. The framework employs a hybrid approach, combining LLMs with dedicated libraries to ensure efficient on-device execution. Our approach is based on a two-step process: first, a fine-tuned LLM converts user commands into structured JSON policy representations; then, a transformation module translates the JSON policies into fully compliant U-XACML policies. To ensure generality across different domains, we introduce a taxonomy-driven dataset creation, which enables policy creation for different environments such as smart homes, smart offices, and healthcare settings. Our evaluation demonstrates that the system achieves 93 % accuracy in policy generation and 91 % accuracy when handling ambiguous or noisy inputs. It also reaches 98 % agreement with expert-defined policies in real-world scenarios. Finally, on-device performance evaluations confirm the feasibility of running the model in practical settings, demonstrating reliable inference under constrained hardware conditions.

This paper proposes a novel approach for multi-party collaborative data analysis problems, where analysis accuracy and divergence are required, as well as both privacy of shared data and explainability of results. The proposed approach aims at trading-off data privacy, decision explainability, and data utility by analytically relating these three measures, evaluating how they impact each other, and proposing a methodology to find the best possibletrade-offamong them. In particular, given a set of requirements from the participants for a collaborative analysis problem, we propose a method to properly tune the parameters of privacy-preserving mechanisms and explainability techniques to be adopted by all participants, obtaining the besttrade-off. The paper is focused on deep learning-based image data analysis problems, though the approach can be generalized to other data types. The$(\epsilon , \delta )$-Differential Privacyand theAutoencodersprivacy-preserving techniques have been adopted to preserve data privacy, while theSmoothGradmechanism has been used to provide decision explainability. The proposed methodology has been validated with a set of experiments on three multi-class deep learning classifiers and three well-known image datasets, MNIST, FER, and CIFAR-10.

In this paper, we present further contributions to the field of privacy-preserving and explainable data analysis applied to tabular datasets. Our approach defines a comprehensive optimization criterion that balances the key aspects of data privacy, model explainability, and data utility. By carefully regulating the privacy parameter and exploring various configurations, our methodology identifies the optimal trade-off that maximizes privacy gain and explainability similarity while minimizing any adverse impact on data utility. To validate our approach, we conducted experiments using five classifiers on a binary classification problem using the well-known Adult dataset, which contains sensitive attributes. We employed (ϵ, δ)-differential privacy with generative adversarial networks as a privacy mechanism and incorporated various model explanation methods. The results showcase the capabilities of our approach in achieving the dual objectives of preserving data privacy and generating model explanations.

Non-Fungible Tokens (NFTs) are currently used in a large number of scenarios, from digital art to the metaverse, to trace the ownership of assets exchanged between users. However, most NFT defining standards, such as the widely adopted ERC 721 for the Ethereum protocol, have been designed with immutable assets only in mind. As such, they are not suitable for representing assets with features that may need to be updated during their lifetime. To overcome this issue, in the literature have been proposed new models that properly represent and protect mutable assets through NFTs, such as Non Fungible Mutable Tokens, NMTs. In this paper, we expanded the NMT model with a security support meant to protect assets’ features updates through access control policies that are defined by the asset creator and the current asset owner and enforced during the assets’ lifetime. Policing updates is of paramount importance, because it protects the asset from unintended updates that could greatly alter the asset itself and its value. The main contributions of this paper are a detailed description of the NMT smart contracts architecture and internal dependencies, as well as an experimental validation of NMTs by providing the implementation of a NMT representing a wearable (a jacket) in Decentraland, a popular metaverse environment.

Attribute-Based Access Control (ABAC) systems are commonly used for protecting resources from unauthorized accesses. They perform the access decision process taking into account a set of attributes describing the features of the subjects executing the accesses, the resources being accessed, and the environment. In this paper, we propose to enhance traditional ABAC systems by integrating them with the novel Self Sovereign Identity (SSI) paradigm. More precisely, we propose a novel access control framework, leveraging the well known eXtensible Access Control Markup Language (XACML) standard for ABAC systems, which allows to exploit SSI credentials as sources of users’ attributes for policy evaluation. Finally, we showcase a prototype implementation of our proposal, and we analyze its performance when evaluating XACML policies integrating SSI credentials with a variable number of attributes.

A blockchain is a data structure consisting of a list of blocks containing transactions and maintained by a network of nodes in a decentralized manner. In permissionless blockchains, anyone can contribute to the decentralization and security of the transactions. With the advent of smart contracts, programs whose execution is replicated by all the nodes of the network, the blockchain can be deemed not only a reliable and auditable data repository, but also a secure and verifiable computational infrastructure. However, due to the aforementioned features, the throughput of most permissionless blockchains is low, and executing a smart contract can be expensive, depending on its computational complexity. To mitigate these issues, a popular research line studies the implementation of Layer 2 solutions, which consists of nodes that operate off-chain yet remaining tethered to the blockchain. Our literature analysis revealed that a majority of the research articles surveying Layer 2 technologies and solutions typically classify them on the basis of the Layer 2 operations they perform, as well as their ability to improve the processing capacity of the blockchain. In this paper, instead, we survey the methodologies that provide a secure binding between Layer 2 and the blockchain. We refer to these binding techniques as “proving schemes” which we classify as: data integrity proofs, validity proofs, and fraud proofs. For each proving scheme, we describe its intended purpose, the advantages it offers, the methodologies commonly used to connect the operations performed at Layer 2 with the blockchain, and the applications that benefit from such scheme. Finally, we discuss and compare them to give a general comprehension about how schemes can satisfy general requirements common to most Decentralized Applications.

The Self-Sovereign Identity (SSI) is a novel paradigm aimed at giving back users sovereignty over their digital identities. Adopting the SSI approach prevents users to have a distinct identity for each service they use, instead, use a unique decentralised identity for all the services they need to access. However, to really benefit from the SSI advantages, an actual decentralised implementation is needed to fit the specific requirements and limits of decentralised architectures, such as blockchain. To this aim, this paper proposes Algorand Identity (AlgoID), a new SSI framework for the Algorand blockchain which differs from the already existing one, because it is fully blockchain based, i.e., it exploits Algorand itself for the storage of the data identity and as the registry location. The proposed framework has been completely implemented and validated through experiments, showing that the time required to execute the framework operations is acceptably low in realistic use cases.

In recent years, Decentralized Online Social Networks (DOSNs) have been attracting the attention of many users because they reduce the risk of censorship, surveillance, and information leakage from the service provider. In contrast to the most popular Online Social Networks, which are based on centralized architectures (e.g., Facebook, Twitter, or Instagram), DOSNs are not based on a single service provider acting as a central authority. Indeed, the contents that are published on DOSNs are stored on the devices made available by their users, which cooperate to execute the tasks needed to provide the service. A specific form of cooperation is to store the content published by a user on other peers’ devices as well, hence dramatically enhancing availability. Consequently, such contents must be properly protected by the DOSN infrastructure, in order to ensure that they can be really accessed only by users who have the permission of the publishers. As a consequence, DOSNs require efficient solutions for protecting the privacy of the contents published by each user with respect to the other users of the social network. This is exactly the focus of this paper. In particular, we investigate and compare the principal content privacy enforcement models adopted by current DOSNs evaluating their suitability to support different types of privacy policies based on user groups. Such evaluation is carried out by implementing several models and comparing their performance for the typical operations performed on groups, i.e., content publish, user join, and user leave. In detail, we show that the join operation incurs a similar cost for all the privacy enforcement models and groups, while for the leave operation performance is greatly affected by the selected solution, which must be evaluated on a case-by-case basis depending on both the type and the activity level of the group—as analytically detailed in our contribution. Further, we also highlight the limitations of current approaches and show future research directions. The provided contributions, other than being interesting on their own, set a blueprint for researchers and practitioners interested in implementing DOSNs, and highlight a few open research directions.

Summary Cyber‐attacks are considered the most significant threat to organizations from different sectors, including critical infrastructure. Access to critical assets, including industrial control systems, and control over their usage is one of the security approaches implemented to protect those systems from unauthorized access. However, existing implementations do not support the enforcement of fine‐grained authorization policies and do not provide continuous control over data access. Furthermore, existing implementations of the access control paradigm require policy‐makers to perform a manual update of policies that do not consider information about potential or ongoing cyber attacks. In this work, we propose a framework that enables continuous control on the execution of access rights in the industrial domain. Furthermore, the framework relies on cyber incident information shared by trusted entities. This information is used for updating security policies in order to prevent possible incidents within the smart factory infrastructure. We also provide experimental results that show the operability and the efficiency of the proposed framework.

Nowadays, creating a blockchain-based system for supply chain tracing is a complex task. This paper defines a model, a graphical domain specific language, and a set of tools aimed at helping supply chain domain experts to create blockchain based tracing systems for their supply chains. Starting from a graphical representation of the supply chain, the solidity smart contracts implementing the related tracing system are automatically generated by our framework. Small interventions of programmers are required to customize and finalize such smart contracts. A set of web based interfaces to interact with such smart contracts are also automatically generated. We are confident that our results will increase blockchain usage for supply chain traceability thanks to the automatic process of smart contract generation.

Recent years have witnessed, especially in Europe, a shift aimed at bringing users back at the center of digital systems. This has driven innovation towards the affirmation of decentralized systems, in line with the Self Sovereign Identity paradigm. User control over the consumption and disclosure of their data is a key topic of such drive. In this paper we show how it is possible to apply this increasingly popular concept to a traditionally centralized and opaque digital process: Access Control systems. To this aim we expand the XACML standard for Attribute Based Access Control systems with the novel concept of private attributes, i.e. attributes whose values should not be disclosed while still contributing to a policy evaluation result after user consent. Basing our proposal on blockchain systems, we show how to leverage smart contracts and zero knowledge proofs to allow for transparent policies evaluation without disclosing the value of such sensible attributes. Beside formalizing our goals, presenting the system architecture, and discussing its advantages and drawbacks with respect to the traditional model, we provide a reference example to show our proposal innovative capabilities and provide a prototype experimental evaluation to prove its feasibility.

The blockchain technology has been gaining an increasing popularity for the last years, and smart contracts are being used for a growing number of applications in several scenarios. The execution of smart contracts on public blockchains can be invoked by any user with a transaction, although in many scenarios there would be the need for restricting the right of executing smart contracts only to a restricted set of users. To help deal with this issue, this article proposes a system based on a popular access control framework called RT, Role-based Trust Management, to regulate smart contracts execution rights. The proposed system, called Layer 2 DecentrAlized Role-based Trust management (L2DART), implements the RT framework on a public blockchain, and it is designed as a layer-2 technology that involves both on-chain and off-chain functionalities to reduce the blockchain costs while keeping blockchain auditability, i.e., immutability and transparency. The on-chain costs of L2DART have been evaluated on Ethereum and compared with a previous solution implementing on-chain all the functionalities. The results show that the on-chain costs of L2DART are relatively low, making the system deployable in real-world scenarios.

This paper proposes a novel approach for privacy preserving face recognition aimed to formally define a trade-off optimization criterion between data privacy and algorithm accuracy. In our methodology, real world face images are anonymized with Gaussian blurring for privacy preservation. The anonymized images are processed for face detection, face alignment, face representation, and face verification. The proposed methodology has been validated with a set of experiments on a well known dataset and three face recognition classifiers. The results demonstrate the effectiveness of our approach to correctly verify face images with different levels of privacy and results accuracy, and to maximize privacy with the least negative impact on face detection and face verification accuracy.

Every person has associated a set of attributes that need to be shown to perform daily operations, such as the ones in personal identity card. The Self Sovereign Identity framework brings this to the digital world, giving to the user full responsibility of their own attributes. Each user receives a set of attributes is paired with as a Verifiable Credential, which could be issued, for example, by a municipality as a digitally signed ID card. However, it is not possible to disclose only one attribute of the ID card without invalidating the signature, therefore Selective Disclosure approaches have been defined to address this problem. This paper describes a selective disclosure method based on hashing, showing that the method is secure and applicable to the real world due to its low requirement in terms of space and time to create and verify a credential.

Summary In this article we present an application of the Usage Control paradigm to a Smart Home infrastructure, based on a model extension and structured use of obligations. In the proposed extended model obligations are exploited to enforce two different access revocation time, namely revoke and suspend. This increases the policy expressiveness and enable to optimize the resource usage. Furthermore, obligations are exploited to send commands via IFTTT to different interconnected Smart Home devices, to impose safety‐relevant behaviors, or to act on policy attributes to implement a self‐healing paradigm for revoked sessions. The article is motivated by a parental control use case where deep learning is used in combination with Usage Control to regulate dynamically viewing rights of a smart‐TV and interactions with interconnected devices. Accuracy and performance experiments show the effectiveness and feasibility of the proposed work.

The Lightning Network is a P2P overlay that allows two Bitcoin users to create a new payment channel with a transaction, and later exchange cryptocurrency on that channel independently from the Bitcoin blockchain, improving Bitcoin’s scalability issues and making it more suitable for frequent micropayments. Moreover, the channels can also be used to route payments between two users who do not share a direct channel themselves.In this paper we study the topology of the Lightning Network in a given time period, performing a number of analysis on the channels and on the nodes composing the network. We study the churn rate, we evaluate centrality measurements and the clustering coefficient to evaluate the network connectivity, and finally we analyze the presence of a pattern that we named "bouquet". The importance of this pattern is proven by the finding that, removing specific nodes of the "bouquets" (1% of the total) causes the disconnection from the largest component of about 41% nodes.

In the past years, trust management systems have been proven suitable for solving the authorization problem in distributed systems, such as peer to peer systems, social networks, cloud, mobile ad-hoc networks, and Internet of things. Trust management systems could be either managed by a central authority or decentralized. In both cases the entity or, respectively, the set of entities managing the system need to be trusted for all users. To overcome this limitation, this paper brings blockchain technology into trust management systems, proposing a novel implementation of the Role-based Trust management framework (RT) on blockchain. The approach relies on smart contracts to represent user trust networks and to infer new trust relations through the chain discovery algorithm. We evaluated a prototype implemented on Ethereum on a representative set of policies related to different scenarios.

Recommender Systems (RSs) are becoming increasingly popular in the last years. They collect reviews concerning several types of items (e.g., shops, professionals, services, songs or videos) in order to rank them according to a given criterion, and to suggest the most relevant ones to their users. However, most of the currently used RSs exhibit two main drawbacks: they are based on a centralized control model and they do not provide reward mechanisms to encourage the participation of users. To deal with these challenges, the architectures of current RSs could be enhanced through blockchain technology, thus providing novel solutions to decentralize them. As a matter of fact, the blockchain technology could be successfully adopted in this context because smart contracts would allow the decentralization of system control, while cryptocurrency and tokens could be used to implement the reward mechanism. In the light of the above considerations, this manuscript presents a decentralized rating framework aimed to support the users of RSs based on blockchain technology, providing a token-based reward mechanism that remunerates users submitting their reviews to incentivize their participation. Moreover, the proposed system provides a flexible strategy to rank items, allowing users to choose among different functions to combine reviews to obtain item ranking. The performance and the cost of using the proposed system have been evaluated on the Ropsten Ethereum test network. For instance, our experiments have shown that the median time required to store a batch of 35 ratings is about 47 s, while the average time required to obtain the score of an item having 6000 ratings is less than 2.5 s.

The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount of information produced by their users, and the corresponding capacity to influence markets, politics, and society, have led both industrial and academic researchers to focus on how such systems could be influenced . While previous work has mainly focused on measuring current influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. Indeed, one of the main characteristics of OSNs is the ability of users to create different groups types, as well as to join groups defined by other users, in order to share information and opinions. In this article, we formulate the Influencers Prediction problem in the context of groups created in OSNs, and we define a general framework and an effective methodology to predict which users will be able to influence the behavior of the other ones in a future time period, based on historical interactions that occurred within the group. Our contribution, while rooted in solid rationale and established analytical tools, is also supported by an extensive experimental campaign. We investigate the accuracy of the predictions collecting data concerning the interactions among about 800,000 users from 18 Facebook groups belonging to different categories (i.e., News, Education, Sport, Entertainment, and Work). The achieved results show the quality and viability of our approach. For instance, we are able to predict, on average, for each group, around a third of what an ex-post analysis will show being the 10 most influential members of that group. While our contribution is interesting on its own and—to the best of our knowledge—unique, it is worth noticing that it also paves the way for further research in this field.

This work presents a framework that provides ongoing control on actions execution in the industrial environment exploiting the OPC Unified Architecture (OPC-UA) framework and the Usage Control (UCON) paradigm. We present a fine-grained usage control model, referred as OPC-UCON, satisfying security and privacy needs of the OPC-UA framework. Our proposed framework exploits the OPC-UA connectivity between simulated industrial components and uses the UCON paradigm for dynamically controlling actions execution according to fine-grained policies reported in the standardized format. The UCON paradigm, in a form of the system, is in charge of controlling the process of dynamic policy reevaluation and the possibility of revoking already granted authorization by stopping previously authorized actions if conditions do not satisfy policy anymore. We presented the implementation and deployment of the proposed framework in a simulated industrial environment with relevant security policies to reflect the advantages of the OPC-UCON model.

Cyber-attacks affect every aspect of our lives. These attacks have serious consequences, not only for cyber-security, but also for safety, as the cyber and physical worlds are increasingly linked. Providing effective cyber-security requires cooperation and collaboration among all the entities involved. Increasing the amount of cyber threat information (CTI) available for analysis allows better prediction, prevention and mitigation of cyber-attacks. However, organizations are deterred from sharing their CTI over concerns that sensitive and confidential information may be revealed to others. We address this concern by providing a flexible framework that allows the confidential sharing of CTI for analysis between collaborators. We propose a five-level trust model for a cloud-edge based data sharing infrastructure. The data owner can choose an appropriate trust level and CTI data sanitization approach, ranging from plain text, through anonymization/pseudonymization to homomorphic encryption, in order to manipulate the CTI data prior to sharing it for analysis. Furthermore, this sanitization can be performed by either an edge device or by the cloud service provider, depending upon the level of trust the organization has in the latter. We describe our trust model, our cloud-edge infrastructure, and its deployment model, which are designed to satisfy the broadest range of requirements for confidential CTI data sharing. Finally we briefly describe our implementation and the testing that has been carried out so far by four pilot projects that are validating our infrastructure.

Distributed Online Social Networks (DOSNs) have been proposed to shift the control over user data from a unique entity, the online social network provider, to the users of the DOSN themselves. In this paper we focus on the problem of preserving the privacy of the contents shared to large groups of users. In general, content privacy is enforced by encrypting the content, having only authorized parties being able to decrypt it. When efficiency has to be taken into account, new solutions have to be devised that: i) minimize the re-encryption of the contents published in a group when the composition of the group changes; and, ii) enable a fast distribution of the cryptographic keys to all the members ($n$) of a group, each time a set of users is removed from or added to the group by the group owner. Current solutions fall short in meeting the above criteria, while our approach requires only $O(d cdot log_d(n))$ encryption operations when a user is removed from a group (where $d$ is an input parameter of the system), and $O(2cdot log_d(n))$ when a user joins the group. The effectiveness of our approach is evaluated through simulations based on a real online social network.

In this demo we show the functionalities of the Open Source MANO extended with advanced authorization capabilities able to enhance the security support in terms of preservation of virtual resources and slices integrity in a dynamic context of users, services and resources. The presented extension consists of a Usage Control System integrated with Open Source MANO enhancing traditional authorization operations with ongoing usage control on established slices, virtual resources and user behaviour by continuously reconsidering the granting of resources in light of mutable attribute of users, resources and environment (e.g., presence of viruses, set-up of weak passwords).

Modern decentralized and distributed environments, as typical from IoT or Industry 4.0 architectures, require a more advanced and granular security management than the currently available standard access control methodologies. Obligations, as defined by the Usage Control model, have been introduced to enhance the traditional access control security mechanisms by imposing the execution of policy-determined actions. This paper presents an extension of the architecture of the Usage Control system already existing in literature, which aims at formalizing the management of Obligations. Three additional components, naturally integrated within the Usage Control system, verify and/or ensure the correct enforcement of obligations also allowing their effect to be evaluated in the continuous access decision making process. The proposed extension thus allows to verify complex conditions when evaluating obligation- specific attributes extracted from the domain where obligations are enforced or observed.

This work proposes to exploit blockchain technology to define Access Control systems that guarantee the auditability of access control policies evaluation. The key idea of our proposal is to codify attribute-based Access Control policies as smart contracts and deploy them on a blockchain, hence transforming the policy evaluation process into a completely distributed smart contract execution. Not only the policies, but also the attributes required for their evaluation are managed by smart contracts deployed on the blockchain. The auditability property derives from the immutability and transparency properties of blockchain technology. This paper not only presents the proposed Access Control system in general, but also its application to the innovative reference scenario where the resources to be protected are themselves smart contracts. To prove the feasibility of our approach, we present a reference implementation exploiting XACML policies and Solidity written smart contracts deployed on the Ethereum blockchain. Finally, we evaluate the system performances through a set of experimental results, and we discuss the advantages and drawbacks of our proposal.

With the rapid development and growth of online social networks (OSNs), researchers have been pushed forward to improve the knowledge of these complex networks by analyzing several aspects, such as the types of social media, the structural properties of the network, or the interaction patterns among users. In particular, a relevant effort has been devoted to the study and identification of cohesive groups of users in OSNs (also referred as communities) because they are the basic building block of each OSN. While several research works on groups in OSNs have mainly focused on identifying the types of groups and the contents created by their members, the analysis of internal organizations of such groups remains unexplored due to the lack of real data sets containing information about such groups, about their members, and the interactions among them. In this article, we compensate for this shortcoming by studying the main properties of groups defined in OSNs, taking as reference use cases 40 real Facebook groups of different categories that account for a total of about 500.000 users. In particular, we exploit interaction patterns among users and social network analysis to uncover interesting aspects related to the internal organization of groups. Experimental results reveal that the majority of the collected groups exhibit an internal structure where members can be clustered in four subgroups according to the level of tie strength of the relations they have. Furthermore, clusters identified on Facebook groups can provide relevant information about the importance of users within such groups.

Collaborative smart services provide functionalities which exploit data collected from different sources to provide benefits to a community of users. Such data, however, might be privacy sensitive and their disclosure has to be avoided. In this paper, we present a distributed multi-tier framework intended for smart-environment management, based on usage control for policy evaluation and enforcement on devices belonging to different collaborating entities. The proposed framework exploits secure multi-party computation to evaluate policy conditions without disclosing actual value of evaluated attributes, to preserve privacy. As reference example, a smart-grid use case is presented.

Summary The understanding of the user temporal behavior is a crucial aspect for all those systems that rely on user resources for daily operations, such as decentralized online social networks (DOSNs). Indeed, DOSNs exploit the devices of their users to take on and share the tasks needed to provide services such as storing the published data. In the last years, the increasing popularity of DOSN services has changed the way of how people interact with each other by enabling users to connect to these services at any time by using their personal devices (such as notebooks or smartphones). As a result, the availability of data in these systems is strongly affected (or reflected) by the temporal behavior of their users in terms of connections to DOSNs. In this paper, we propose the use of linear predictors to address the problem of the availability of user devices and, hence, data in DOSNs. To validate the proposed approaches, we evaluated their performance conducting a set of simulations exploiting a dataset of temporal information concerning the connections to Facebook collected from a set of users.

Attributes describing the features of subjects, objects and of the environment are used in access and usage control models to determine the right of a subject to use an object in a given environment. Hence, it is crucial for the effective enforcement of access and usage policies that authorization systems are able to promptly retrieve the values of the required attributes from the Attribute Providers. However, sometimes attribute providers could not respond when queried by Authorization systems, because they could be temporary down or unreachable. This could affect the decision processes, causing some requests to be unduly denied or some ongoing accesses to be unduly interrupted. This paper proposes a strategy that can be adopted by an Authorization system to estimate the value of the attributes it requires when the corresponding attribute providers are not responding. This strategy leverages on the collaboration of the other Authorization systems which exploit the same attribute providers, and which could have cached a value for the required attributes. We validate the presented approach through a set of simulative experiments which consider the presence of malicious authorization systems in the cooperative environment.

Distributed Online Social Networks (DOSNs) have been recently proposed as an alternative to centralized solutions to allow a major control of the users over their own data. Since there is no centralized service provider which decides the term of service, the DOSNs infrastructure exploits users’ devices to take on the online social network services. In this paper, we propose a data allocation strategy for DOSNs which exploits the privacy policies of the users to increase the availability of the users’ contents without diverging from their privacy preferences. A set of replicas of the profile’s content of a user U are stored on the devices of other users who are entitled to access the profile according to U’s privacy policies. The experimental results obtained from the simulations on traces taken from a real social network show the effectiveness of our approach.

Distributed Online Social Networks (DOSNs) have recently been proposed to shift the control over user data from a unique entity to the users of the DOSN themselves. In this paper, we focus our attention on the problem of privacy preserving content sharing to a large group of users of the DOSNs. Several solutions, based on cryptographic techniques, have been recently proposed. The main challenge here is the definition of a scalable and decentralized approach that: i) minimizes the re-encryption of the contents published in a group when the composition of the group changes and ii) enables a fast distribution of the cryptographic keys to all the members (n) of a group, each time a new user is added or removed from the group by the group owner. Our solution achieves the above goals, providing performance unattained by our competitors. In particular, our approach requires only O(d·logn) encryption operations when the group membership changes (eviction), and only O(2·logn) when a join occurs (where d is an input parameter of the system). The effectiveness of our approach is evaluated by an experimental campaign carried out over a set of traces from a real online social network.

The increasing need for physical security in critical environment has led to a widespread of video surveillance systems. Effective video surveillance systems should be able to detect the presence of unauthorized people in the monitored environments while preserving the privacy of authorized ones. To this aim, our paper proposes the adoption of the usage control model in the video surveillance scenario to enforce security policies that continuously control whether a person holds the right to stay in a give space (e.g., a room) from the moment when this person enters that space. In some scenarios, a person is allowed to stay in the room only under some circumstances, which are described by the usage control policy. When the policy is violated an action is taken, e.g., the video camera placed in the room enables the registration. This paper presents the architecture of the proposed framework, provides an example of usage control policy in a real scenario, and describes the main details of our prototype implementation.

This paper is the report on the 1st International Workshop on TEchnical and LEgal aspects of data pRIvacy and SEcurity (TELERISE 2015) at the 37th International Conference on Software Engineering (ICSE 2015). TELERISE investigates privacy and security issues in data sharing from a technical and legal perspective. Keynote speech as well as selected papers presented at the event fit the topics of the workshop. This report gives the rationale of TELERISE and it provides a provisional program.

Data sharing is crucial in many aspects of nowadays life, from economy to leisure, from public administration to healthcare. However, it implies several privacy issues that have to be managed. Definition of appropriate policies helps to safeguard the data privacy. This paper describes an expertise-driven authoring tool for privacy policies to be applied to the healthcare scenario. The tool exhibits two different interfaces, designed according to specific expertise of the policy authors. It is part of a general framework for editing, analysis, and enforcement of privacy policies. Furthermore, this serves as a first brick for a usability study on such tools.

Recent studies report that Internet users are growingly looking for health information through the Wikipedia Medicine Portal, a collaboratively edited multitude of articles with contents often comparable with professionally edited material. Automatic quality assessment of the Wikipedia medical articles has not received much attention by Academia and it presents open distinctive challenges. In this paper, we propose to tag the medical articles on the Wikipedia Medicine Portal, clearly stating their maturity degree, intended as a summarizing measure of several article properties. For this purpose, we adopt the Analytic Hierarchy Process, a well known methodology for decision making, and we evaluate the maturity degree of more than 24000 Wikipedia medical articles. The obtained results show how the qualitative analysis of medical content not always overlap with a quantitative analysis (an example of which is shown in the paper), since important properties of an article can hardly be synthesized by quantitative features. This seems particularly true when the analysis considers the concept of maturity, defined and verified in this work.

The increasing number and complexity of e-health services provided to citizens push healthcare organizations to adopt powerful computing systems to deal with the resulting workload. The adoption of the Cloud to host such services is a promising solution to address the increasing demand for computational power and also to deal with expected or unexpected computational peaks. This paper proposes a framework that allows the execution on the Cloud of the X1.V1 platform, which is a commercial product that supports a number of e-health services as well as the interoperability among different healthcare environments. This framework is meant to enable an easy and quick management of the Cloud resources allocated to the X1.V1 platform, in order to enhance the platform performances and optimize resources utilization. The design of the framework takes into account security aspects as well, because unauthorized accesses could lead to serious inefficiencies of the e-health services.

Cloud Federation is a promising approach to enhance cross-cloud application execution. Nevertheless, such approach emphasizes open challenges in Cloud Computing, such as revoking long-lasting authorization on resources as soon as conditions granting the access right are no longer valid. To tackle this kind of issues, we built a prototype of Cloud Federation that leverages the concept of Usage Control (UCON), by continuously monitoring and reassessing the users right on resources. We exploited an extension of the XACML standard and measured the overhead caused by different security policies and distributions of requests. Results suggest that the UCON model can be effectively applied in Cloud Federations and its performance is sustainable when applied to the relevant actions of the lifecycle of applications.

The Electronic Patient Record (EPR) enables the sharing of medical documents among several and widespread healthcare organizations. To guarantee privacy properties, access control policies should be defined, regulating how the documents can be shared. Conflicts may occur among policies applicable to the same access request. We present a running prototype, based on an XACML engine, that implements a conflict resolution strategy as an extension to the standard combining algorithms of the XACML engine. We evaluate the efficiency of our proposal in terms of execution time, on a variable number of conflicting rules.

Usage Control (UCON) enhances traditional access control introducing mutable attributes and continuous policy enforcement. UCON addresses security requirements of dynamic computer environments like Grid and Cloud, but also raises new challenges. This paper considers two problems of usage control. The first problem arises when a value of a mutable attribute required for an access decision is uncertain. The second problem questions when to retrieve fresh values of mutable attributes and to trigger the access reevaluation during the continuous control. We propose quantitative risk-based methods to tackle these problems. The authorisation system grants the access if the security policy is satisfied and the risk level is acceptable. The authorisation system retrieves fresh attribute values following the strategy which minimises the risk of the usage sessions. We integrate the authorisation system based on the U-XACML language with quantitative methods for risk evaluation. We present the architecture, the implementation, and the evaluation of the overhead posed by the risk computation.

The Session Initiation Protocol (SIP) is a signaling protocol for the creation, modification and termination of multimedia sessions with one or more participants. While SIP operates in highly dynamic environments such as Next Generation Networks, in current deployments its access control support is based on traditional access control models. The main problem these models face is that under certain circumstances they tend to be inadequate in current highly dynamic environments. Usage Control is an access control model that supports the same functionalities as traditional models do, but further introduce novel ones. In a previous work, an architecture supporting continuous authorization on SIP based on the Usage Control model was presented. Here this architecture is further elaborated, described in more detail and experimentally evaluated.

In this paper we describe our general framework for usage control (UCON) enforcement on GRID systems. It allows both GRID services level enforcement of UCON as well as fine-grained one at the level of local GRID node resources. In addition, next to the classical checks for usage control: checks of conditions, authorizations, and obligations, the framework also includes trust and risk management functionalities. Indeed, we show how trust and risk issues naturally arise when considering usage control in GRID systems and services and how our architecture is flexible enough to accommodate both notions in a pretty uniform way. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper introduces a formal model, an architecture and a prototype implementation for usage control on GRID systems. The usage control model (UCON) is a new access control paradigm proposed by Park and Sandhu that encompasses and extends several existing models (e.g. MAC, DAC, Bell-Lapadula, RBAC, etc.). Its main novelty is based on continuity of the access monitoring and mutability of attributes of subjects and objects. We identified this model as a perfect candidate for managing access/usage control in GRID systems due to their peculiarities, where continuity of control is a central issue. Here we adapt the original UCON model to develop a full model for usage control in GRID systems. We use as policy specification language a process description language and show how this is suitable to model the usage policy models of the original UCON model. We also describe a possible architecture to implement the usage control model. Moreover, we describe a prototype implementation for usage control of GRID computational services, and we show how our language can be used to define a security policy that regulates the usage of network communications to protect the local computational service from the applications that are executed on behalf of remote GRID users.

This paper advocates virtualization technology as a methodology to solve the security problems that an organization has to face when contributes with its resources to a grid. In particular, this technology makes it possible to increase the overall security of any system by inserting a set of controls into the code that implements one virtual machine. In this way, a secure cooperation among virtual machine can be implemented. This generalizes the current approach that exploits virtualization only for the confinement of alternative programming environments resulting from the partitioning of a physical machine into a set of non-cooperating virtual machines. The ability to support cooperation among virtual machines may be used to define networks of cooperating virtual machines to execute distributed applications. The paper describes a general purpose approach to security based upon virtual networks of cooperating virtual machines and applies it to one of the most challenging problems: that of securing a grid environment