Related papers: Where Trust Fails: Mapping Location-Data Provenanc…
Creative works, whether paintings or memes, follow unique journeys that result in their final form. Understanding these journeys, a process known as "provenance analysis", provides rich insights into the use, motivation, and authenticity…
Inspired by the boom of the consumer IoT market, many device manufacturers, start-up companies and technology giants have jumped into the space. Unfortunately, the exciting utility and rapid marketization of IoT, come at the expense of…
Data spaces represent an emerging paradigm that facilitates secure and trusted data exchange through foundational elements of data interoperability, sovereignty, and trust. Within a data space, data items, potentially owned by different…
Cognitive radio networks (CRNs) have emerged as an essential technology to enable dynamic and opportunistic spectrum access which aims to exploit underutilized licensed channels to solve the spectrum scarcity problem. Despite the great…
Data security, which is concerned with the prevention of unauthorized access to computers, databases, and websites, helps protect digital privacy and ensure data integrity. It is extremely difficult, however, to make security watertight,…
The rapid deployment of AI systems in high-stakes domains, including those classified as high-risk under the The EU AI Act (Regulation (EU) 2024/1689), has intensified the need for reliable compliance auditing. For binary classifiers,…
Recent European efforts around digital identity -- the EUDI regulation and its OpenID architecture -- aim high to provide an EU-wide authentication framework. However, its current technical and legislative architecture are based on a…
As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However,interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this…
The burgeoning of misleading or false information spread by untrustworthy websites has, without doubt, created a dangerous concoction. Thus, it is not a surprise that the threat posed by untrustworthy websites has emerged as a central…
Digital Twins (DTs) are increasingly deployed across application domains, yet the treatment of trust-related issues remains unevenly addressed. To examine whether and how trust is discussed in the current landscape, we conducted a…
Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data…
This research presents a methodology for trusting the provenance of data on the web. The implication is that data does not change after publication and the source of the data is stable. There are different data that should not change over…
This article surveys blockchain-based approaches for several security services. These services include authentication, confidentiality, privacy, and access control list (ACL), data and resource provenance, and integrity assurance. All these…
As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However,…
This paper explores the evolving landscape of data spaces, focusing on key concepts, practical applications, and emerging future directions. It begins by introducing the foundational principles that underpin data space architectures,…
This survey examines intelligent forensics in next-generation mobile networks, arguing that future wireless security must move beyond real-time detection toward accountable post-incident reconstruction. Unlike traditional digital forensics,…
Data sharing is the fuel of the galloping artificial intelligence economy, providing diverse datasets for training robust models. Trust between data providers and data consumers is widely considered one of the most important factors for…
The rapid growth of artificial intelligence (AI) technologies has raised major privacy and ethical concerns. However, existing AI incident taxonomies and guidelines lack grounding in real-world cases, limiting their effectiveness for…
The prevalence of mobile devices and Location-Based Services (LBS) necessitate the study of Location Privacy-Preserving Mechanisms (LPPM). However, LPPMs reduce the utility of LBS due to the noise they add to users' locations. Here, we…
While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world applications. We…