Related papers: Zero-Trust Foundation Models: A New Paradigm for S…
Blockchain is a promising technology for establishing trust in IoT networks, where network nodes do not necessarily trust each other. Cryptographic hash links and distributed consensus mechanisms ensure that the data stored on an immutable…
Foundation models (FMs), such as GPT-4 and AlphaFold, are reshaping the landscape of scientific research. Beyond accelerating tasks such as hypothesis generation, experimental design, and result interpretation, they prompt a more…
Integrating Foundation Models (FMs) into recommendation systems is an emerging and promising research direction. However, centralized paradigms face growing pressure from privacy concerns and strict regulatory requirements. Federated…
The widespread use of the Internet of Things has led to the development of large amounts of perception data, making it necessary to develop effective and scalable data analysis tools. Federated Learning emerges as a promising paradigm to…
By focusing on immersive interaction among users, the burgeoning Metaverse can be viewed as a natural extension of existing social media. Similar to traditional online social networks, there are numerous security and privacy issues in the…
Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in…
The integration of Internet of Things (IoT) devices in healthcare applications has revolutionized patient care, monitoring, and data management. The Global IoT in Healthcare Market value is $252.2 Billion in 2023. However, the rapid…
Data marketplaces (DMs) promote the benefits of the Internet of Things (IoT) in smart cities. To facilitate the easy exchanges of real-time IoT data streams between device owners and third-party applications, it is required to provide…
The accelerating adoption of large language models, retrieval-augmented generation pipelines, and multi-agent AI workflows has created a structural governance crisis. Organizations cannot govern what they cannot see, and existing compliance…
The exponential growth of the Internet of Things (IoT) ecosystem has amplified concerns regarding device reliability, interoperability, and security assurance. Despite the proliferation of IoT security guidelines, a unified and quantitative…
The rapid proliferation of IoT nodes equipped with microphones and capable of performing on-device audio classification exposes highly sensitive data while operating under tight resource constraints. To protect against this, we present a…
Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. FL can be a scalable machine learning solution in big…
To support future intelligent multifunctional sixth-generation (6G) wireless communication networks, Synesthesia of Machines (SoM) is proposed as a novel paradigm for artificial intelligence (AI)-native intelligent multi-modal…
The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks has significantly expanded the attack surface for cyber threats, including botnet-driven…
Autonomous vehicles (AVs) rely on pervasive connectivity to enable cooperative and safety-critical applications, but this connectivity also exposes them to a wide range of cybersecurity threats. Existing perimeter-based security and…
With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence (AI) impractical in many…
Internet of Things (IoT) consists of a large number of devices connected through a network, which exchange a high volume of data, thereby posing new security, privacy, and trust issues. One way to address these issues is ensuring data…
The increased connectivity and potential insider threats make traditional network defense vulnerable. Instead of assuming that everything behind the security perimeter is safe, the zero-trust security model verifies every incoming request…
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large…
We present a passport-level trust token for Europe. In an era of escalating cyber threats fueled by global competition in economic, military, and technological domains, traditional security models are proving inadequate. The rise of…