Efficiency and interoperability are essential for the evolution of secure and scalable e-governance systems. This paper presents a novel framework for interoperable smart contract generation that integrates semantic technologies, and Layer-2 blockchain scaling solutions to enhance interoperability, security, and efficiency in e-governance applications. Using zero-knowledge proofs for privacy-preserving transactions and self-sovereign identity mechanisms for decentralized authentication, the proposed architecture ensures trust and compliance with international standards. Initially applied to e-voting, this framework is adaptable to broader public services, fostering a transparent, cost-effective, and sustainable digital governance ecosystem.

ERATOSTHENES is an EC, co-funded, research project strongly considering modern security challenges in the domain of Internet of Things in mind of their huge penetration into our day to day lives. There are a series of recent challenges that recently have been converted into obstacles or risk points that could block the secure operation of IoT networks in all day to day activities, from home to office, to leisure and security. These include examples such as the highly increased number of connected devices (at all network levels) that are on top forming inhomogeneous networks and systems of systems. Different vendor characteristics further increase the attack surface that is expected to further rise in the upcoming years. Such, highly critical, characteristics, dramatically increase the needs for confidentiality access control, user and things’ privacy, devices’ trustworthiness and compliance that require lifecycle considerations. The ERATOSTHENES project orchestrates a novel distributed, automated, auditable, yet privacy-respectful, Trust and Identity Management Framework and Reference Architecture with the ultimate scope to dynamically and holistically manage IoT devices in a lifecycle approach, strengthening trust, identities, and resilience in the entire IoT ecosystem while supporting the enforcement of the NIS directive, GDPR and Cybersecurity Act. This publication describes the ERATOSTHENES technical concept and reference architecture as well as design considerations, architecture characteristics, connectivity and interoperability.

Over the years several organizations are migrating to Role-Based Access Control (RBAC) as a practical solution to regulate access to sensitive information. Role mining has been proposed to automatically extract RBAC policies from the current set of permissions assigned to users. Existing role mining approaches usually require that this set of permissions is retrievable and complete. Such an assumption, however, cannot be met in practice as permissions can be hard-coded in the applications or distributed over several subsystems. In those cases, permissions can be obtained from activity logs recording the actions performed by users. This, however, can provide an incomplete representation of the permissions within the system. Thus, existing role mining solutions are not directly applicable. In this work, we study the problem of role mining with incomplete knowledge. In particular, we investigate approaches for two instances of the role mining problem with missing values. Moreover, we study metrics to properly evaluate the obtained RBAC policies. We validate the investigated approaches using both synthetic and real data.

The detection and handling of data leakages is becoming a critical issue for organizations. To this end, data leakage solutions are usually employed by organizations to monitor network traffic and the use of portable storage devices. However, these solutions often produce a large number of alerts, whose analysis is time-consuming and costly for organizations. To effectively handle leakage incidents, organizations should be able to focus on the most severe incidents. Therefore, alerts need to be analyzed and prioritized with respect to their severity. This work presents a novel approach for the quantification of data leakages based on their severity. The approach quantifies the severity of leakages with respect to the amount and sensitivity of the leaked information as well as the ability to re-identify the data subjects of the leaked information. To specify and reason on data sensitivity in an application domain, we propose a data model representing the knowledge within the domain. We validate our quantification approach by analyzing data leakages within a healthcare environment. Moreover, we demonstrate that the data model allows for a more accurate characterization of data sensitivity while reducing the efforts for its specification.

Home healthcare services are emerging as a new frontier in healthcare practices. Data reliability, however, is crucial for the acceptance of these new services. This work presents a semi-automated system to evaluate the quality of medical measurements taken by patients. The system relies on data qualifiers to evaluate various quality aspects of measurements. The overall quality of measurements is determined on the basis of these qualifiers enhanced with a troubleshooting mechanism. Namely, the troubleshooting mechanism guides healthcare professionals in the investigation of the root causes of low quality values.

In this paper a user friendly tool appropriate to get user feedback for the application of binarization algorithms is presented. The human feedback is very useful in order to apply next the algorithm to similar images. The tool supports Image Selection and Display, Selection of Binarization Algorithm and Parameter Configuration, Feedback gathering and Creation of log file for further processing.