Bias and unfairness in Machine Learning (ML) are challenging to detect and mitigate, particularly in critical fields such as finance, hiring, and healthcare. While numerous unfairness mitigation techniques exist, most evaluation frameworks assess only a limited set of fairness metrics, primarily focusing on the trade-off between fairness and accuracy. We introduce FAIR-CARE, a new open-source and robust approach that consists of an evaluation pipeline designed for the systematic assessment of unfairness mitigation techniques. Our approach simultaneously evaluates multiple fairness and performance metrics across various ML models. We conduct a comparative analysis on healthcare datasets with diverse distributions—including target class, protected attribute, and their joint distributions—to identify the most effective mitigation technique for each processing type (pre-, in-, and post-processing). Furthermore, we determine the best-performing techniques across different datasets, fairness metrics, performance metrics, and ML models. Finally, we provide practical insights into the application of these techniques, offering actionable guidance for both researchers and practitioners. • Fairness and accuracy can coexist in machine learning models, even on unbalanced datasets. • Mitigation technique performance varies by dataset type and processing stage. • FAIR-CARE identifies top-performing mitigation techniques for each processing type and highlights trade-offs where applicable.

Context: Since its enforcement in 2018, the General Data Protection Regulation (GDPR) has continued to shape how organizations, in the European Economic Area, design and operate their data-driven services. Consent management, in particular, remains a cornerstone of compliance, but it has also become increasingly complex with the rise of data-intensive business models, digital health platforms, and AI-powered services. Despite the availability of technical and organizational tools, many companies still struggle to adapt legacy and large-scale processes to meet GDPR’s consent requirements. Knowledge about these processes is often fragmented across organizational silos, and documentation is incomplete, making re-engineering activities both tedious and error-prone. Objectives: Companies relies on experts for the re-engineering and validation of their processes, while a comprehensive method is still missing to support them in verifying the compliance of their processes with consent. To address these challenges, this paper proposes a model-based approach that supports business and privacy experts in aligning operational processes with GDPR consent principles. Methods.: Rather than introducing a new language that would require analysts modeling processes from scratch, our framework, ProPrivacy, builds on the widely adopted Business Process Model and Notation 2.0 (BPMN 2.0) modeling language, allowing analysts to enrich existing models with consent requirements. To mitigate verification errors and reduce the effort in analyzing complex models, ProPrivacy then automatically verifies compliance with key GDPR principles related to specific and freely given consent and data minimization. We demonstrate the applicability and scalability of our approach on realistic processes from the healthcare domain, where the management of sensitive data continues to present critical privacy challenges. Conclusions: The results suggest that automated verification of business processes can not only support organizations in achieving compliance with GDPR but also serve as a foundation for certifying accountable and transparent business processes.

Data ecosystems are often used in many social, industrial, and research areas to boost the learning process or enhance the value of the endless amount of data generated and collected daily. Their design principles are consolidated, but today, data ecosystems must address new needs, as they are faced with multiple interdependent challenges, such as users’ engagement, intellectual property, data confidentiality, and data sharing. The achievement of these business objectives highly depends on the deployed policies that govern many, if not all, aspects of the data ecosystems. Identifying the role of these policies in achieving data ecosystems’ objectives is key to their success. This paper analyzes policies and their relations to define how they impact the business objectives of the systems and, possibly, to improve them. The proposed method highlights the interdependencies between different policies. Such results will be leveraged to tune policies to achieve data ecosystems’ business objectives.

Collaborative business processes can be seen as smart contracts, as they are oftentimes adopted to express agreements among different organizations. Indeed, they provide mechanisms to formalize the obligations of each involved party. For instance, collaborative business processes can specify when a certain task should be executed, under which conditions a service should be offered to the other participants, and how physical objects and information should be manipulated. In this setting, to prevent misuse of smart contracts and services and information provided, it is paramount to guarantee by design that security requirements are fulfilled. With the rise in popularity of blockchains, several approaches exploiting the trusted smart contract execution environment offered by this technology to enforce collaborative business processes have been proposed. Yet, the complexity of business processes, security requirements, and blockchain applications calls for an engineering approach that guides the design of secure business processes. Such an approach should both take advantage of the possibilities offered by blockchain technology to enforce some security requirements (e.g., non-repudiation), and take into account the limitations blockchain poses for other security requirements (e.g., confidentiality). However, we are not aware of any existing work that aims at addressing such issues following a similar approach. In this article, we propose SecBPMN2BC: a model-driven approach to designing business processes with security requirements that are meant to be deployed on blockchains. SecBPMN2BC consists of: (i) an extension of BPMN 2.0 that allows designing secure smart contracts; (ii) a set of algorithms and their implementation that check incompatible security requirements and help the design of smart contracts; (iii) a workflow that guides the application of the method. The method has been validated with a survey conducted on security and BPMN experts.

Data as a Service (DaaS) offers an effective provisioning model able to exploit the advantages of cloud computing in terms of accessibility and scalability when data providers need to make their data available to different data consumers. Nevertheless, in settings where data are generated at the edge and they need to be propagated (e.g., Industry 4.0, Smart Cities), DaaS model suffers of some limitations: data transfer from the edge to the cloud – and viceversa – could require a significant time and privacy issues could hamper the possibility to move the data. Goal of this paper is to propose a DaaS model based on the Fog Computing paradigm, which combines the advantages of both cloud and edge computing. The proposed solution implements an adaptive multi-agent system where each agent autonomously manages the placement of data in the most convenient location considering the quality of service requirements of the user that it is serving. To guarantee the collaboration of the agents without imposing a centralized control, a reinforcement learning algorithm will be enacted to balance between the local optimum for the single data consumers and the satisfaction of the global requirements of all consumers.

Abstract Requirements are inherently prone to conflicts. Security, data-minimization, and fairness requirements are no exception. Importantly, undetected conflicts between such requirements can lead to severe effects, including privacy infringement and legal sanctions. Detecting conflicts between security, data-minimization, and fairness requirements is a challenging task, as such conflicts are context-specific and their detection requires a thorough understanding of the underlying business processes. For example, a process may require anonymous execution of a task that writes data into a secure data storage, where the identity of the writer is needed for the purpose of accountability. Moreover, conflicts not arise from trade-offs between requirements elicited from the stakeholders, but also from misinterpretation of elicited requirements while implementing them in business processes, leading to a non-alignment between the data subjects’ requirements and their specifications. Both types of conflicts are substantial challenges for conflict detection. To address these challenges, we propose a BPMN-based framework that supports: (i) the design of business processes considering security, data-minimization and fairness requirements, (ii) the encoding of such requirements as reusable, domain-specific patterns, (iii) the checking of alignment between the encoded requirements and annotated BPMN models based on these patterns, and (iv) the detection of conflicts between the specified requirements in the BPMN models based on a catalog of domain-independent anti-patterns. The security requirements were reused from SecBPMN2, a security-oriented BPMN 2.0 extension, while the fairness and data-minimization parts are new. For formulating our patterns and anti-patterns, we extended a graphical query language called SecBPMN2-Q. We report on the feasibility and the usability of our approach based on a case study featuring a healthcare management system, and an experimental user study.

Fog computing is a continuum of resources between the cloud provider and the edge of the network. Such resources can be used to host data and computation that can be moved from the cloud provider to near the consumer in order to increase the quality of the services provided. This tutorial presents a method to select the best data or computation movement to be enacted. The tutorial is divided in three sessions. The first session introduces the fog computing architecture. The second session introduces data and computation movement: their challenges and the available technologies used to enact them. The third session presents the actual method. In particular, how the method uses a goal-based modelling language to define the requirements of consumers, the impact of data and computation movements on their requirements and how it permits to choose the best movement.

Over the last two decades, much attention has been paid to the area of goal-oriented requirements engineering (GORE), where goals are used as a useful conceptualization to elicit, model, and analyze requirements, capturing alternatives and conflicts. Goal modeling has been adapted and applied to many sub-topics within requirements engineering (RE) and beyond, such as agent orientation, aspect orientation, business intelligence, model-driven development, and security. Despite extensive efforts in this field, the RE community lacks a recent, general systematic literature review of the area. In this work, we present a systematic mapping study, covering the 246 top-cited GORE-related conference and journal papers, according to Scopus. Our literature map addresses several research questions: we classify the types of papers (e.g., proposals, formalizations, meta-studies), look at the presence of evaluation, the topics covered (e.g., security, agents, scenarios), frameworks used, venues, citations, author networks, and overall publication numbers. For most questions, we evaluate trends over time. Our findings show a proliferation of papers with new ideas and few citations, with a small number of authors and papers dominating citations; however, there is a slight rise in papers which build upon past work (implementations, integrations, and extensions). We see a rise in papers concerning adaptation/variability/evolution and a slight rise in case studies. Overall, interest in GORE has increased. We use our analysis results to make recommendations concerning future GORE research and make our data publicly available.

Improving e-government services by using data more effectively is a major focus globally. It requires Public Administrations to be transparent, accountable and provide trustworthy services that improve citizen confidence. However, despite all the technological advantages on developing such services and analysing security and privacy concerns, the literature does not provide evidence of frameworks and platforms that enable privacy analysis, from multiple perspectives, and take into account citizens' needs with regards to transparency and usage of citizens information. This paper presents the VisiOn (Visual Privacy Management in User Centric Open Requirements) platform, an outcome of a H2020 European Project. Our objective is to enable Public Administrations to analyse privacy and security from different perspectives, including requirements, threats, trust and law compliance. Finally, our platform-supported approach introduces the concept of Privacy Level Agreement (PLA) which allows Public Administrations to customise their privacy policies based on the privacy preferences of each citizen.

Tracing and integrating security requirements throughout the development process is a key challenge in security engineering. In socio-technical systems, security requirements for the organizational and technical aspects of a system are currently dealt with separately, giving rise to substantial misconceptions and errors. In this paper, we present a model-based security engineering framework for supporting the system design on the organizational and technical level. The key idea is to allow the involved experts to specify security requirements in the languages they are familiar with: business analysts use BPMN for procedural system descriptions; system developers use UML to design and implement the system architecture. Security requirements are captured via the language extensions SecBPMN2 and UMLsec. We provide a model transformation to bridge the conceptual gap between SecBPMN2 and UMLsec. Using UMLsec policies, various security properties of the resulting architecture can be verified. In a case study featuring an air traffic management system, we show how our framework can be practically applied.

Information practices and systems that make use of personal and health-related information are governed by European laws and regulations to prevent unauthorized use and disclosure. Failure to comply with these laws and regulations results in huge monetary sanctions, which both private companies and public administrations want to avoid. How to comply with these laws, requires understanding the privacy requirements imposed on information systems. A holistic approach to privacy requirements specification calls for understanding not only the requirements derived from law, but also citizens' needs with respect to privacy. In this paper, we report on our experience in conducting privacy requirements engineering as part of a H2020 European Project, namely VisiOn (Visual Privacy Management in User Centric Open Requirements) for the development of a privacy platform to improve the interaction between Public Administrations (PA) and citizens, while guarding the privacy of the latter. Specifically, we present the process for eliciting, classifying, prioritizing, and validating privacy requirements for the two types of users, namely PA and citizen. The process is applied to different cases spanning from healthcare to other e-governmental initiatives, with the active involvement of the corresponding PAs. We report on findings and lessons learned from this experience.

Over the last two decades, much attention has been paid to the area of Goal-Oriented Requirements Engineering(GORE), where goals are used as a useful conceptualization to elicit, model and analyze requirements, capturing alternatives and conflicts. Goal modeling has been adapted and applied to many sub-topics within RE and beyond, such as agent-orientation, aspect-orientation, business intelligence, model-driven development, security, and so on. Despite extensive efforts in this field, the RE community lacks a recent, general systematic literature review of the area. As a first step towards providing a GORE overview, we present a Systematic Literature Map, focusing on GORE-related publications at a high-level, categorizing and analyzing paper information in order to answer several research questions, while omitting a detailed analysis of individual paper quality. Our Literature Map covers the 246 top-cited GORE-related conference and journal papers, according to Scopus, classifying them into a number of descriptive paper types and topics, providing an analysis of the data, which is made publicly available. We use our analysis results to make recommendations concerning future GORE research.

Creating and reasoning with goal models is useful for capturing, understanding, and communicating about requirements in the early stages of information system (re)development. However, the utility of goal models is greatly enhanced when an awareness of system intentions can feed into other stages in the requirements analysis process (e.g. requirements elaboration, validation, planning), and can be used as part of the entire system life cycle (e.g., architecture, process design, coding, testing, monitoring, adaptation, and evolution). In order to understand the progress that has been made in integrating goal models with downstream system development, we ask: what approaches exist which map/integrate/transform goal-oriented languages to other software artifacts or languages? To answer this question, we conduct a systematic survey, producing a roadmap of work summarizing 174 publications. Results include a categorization of the “why?” and “how?” for each approach. Findings show that there are a wide variety of proposals with many proposed sources and targets, covering multiple paradigms, motivated by a variety of purposes. We conclude that although much work has been done in this area, the work is fragmented and is often still in a proposal stage.