相关论文: Distributed Ledger for Provenance Tracking of Arti…
Machine learning algorithms learn from data and use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the machine learning process is often difficult to automate. A…
Sustainable urban environments based on Internet of Things (IoT) technologies require appropriate policy management. However, such policies are established as a result of underlying, potentially complex and long-term policy making…
Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI) systems or machine learning (ML) models, the trust lineage…
Blockchain and smart contracts have garnered significant interest in recent years as the foundation of a decentralized, trustless digital ecosystem, thereby eliminating the need for traditional centralized authorities. Despite their central…
Federated learning can solve the privacy protection problem in distributed data mining and machine learning, and how to protect the ownership, use and income rights of all parties involved in federated learning is an important issue. This…
The increasing integration of Artificial Intelligence (AI) into health and biomedical systems necessitates robust frameworks for transparency, accountability, and ethical compliance. Existing frameworks often rely on human-readable, manual…
The development of privacy-enhancing technologies has made immense progress in reducing trade-offs between privacy and performance in data exchange and analysis. Similar tools for structured transparency could be useful for AI governance by…
Artificial Intelligence (AI) systems are increasingly dependent on complex, multi-layered software supply chains that introduce challenges for reproducibility, transparency, and security assurance. This study presents an Artificial…
Increased collaborative production and dynamic selection of production partners within industry 4.0 manufacturing leads to ever-increasing automatic data exchange between companies. Automatic and unsupervised data exchange creates new…
Modern distributed applications in healthcare, supply chain, and the Internet of Things handle a large amount of data in a diverse application setting with multiple stakeholders. Such applications leverage advanced artificial intelligence…
This paper presents a system architecture to promote the development of smart transportation systems. Thanks to the use of distributed ledgers and related technologies, it is possible to create, store and share data generated by users…
A key component of blockchain technology is the ledger, viz., a database that, unlike standard databases, keeps in memory the complete history of past transactions as in a notarial archive for the benefit of any future test. In…
As conversational AI systems become more realistic and widely deployed, users are increasingly uncertain about whether they are interacting with a human or an AI system. When AI identity is unclear, users may unwittingly share sensitive…
Blockchain enables novel, trustworthy Process-Aware Information Systems (PAISs) by enforcing the security, robustness, and traceability of operations. In particular, transparency ensures that all information exchanges are openly accessible,…
"Trustworthy data" is the fuel for ensuring transparent traceability, precise decision-making, and cogent coordination in the supply chain (SC) space. However, the disparate data silos act as a trade barrier in orchestrating the provenance…
The rapid advancement of ML models in critical sectors such as healthcare, finance, and security has intensified the need for robust data security, model integrity, and reliable outputs. Large multimodal foundational models, while crucial…
The adoption of permissioned blockchain networks in enterprise settings has seen an increase in growth over the past few years. While encouraging, this is leading to the emergence of new data, asset and process silos limiting the potential…
Large Language Models (LLMs) and other foundation models are increasingly used as the core of AI agents. In agentic workflows, these agents plan tasks, interact with humans and peers, and influence scientific outcomes across federated and…
In recent years, the advancement in modern technologies has experienced an explosion of huge data sets being captured and recorded in different fields, but also given rise to concerns the security and protection of data storage,…
This Article examines the circumstances in which AI-generated outputs remain linked to their creators and the points at which they lose that connection, whether through accident, deliberate design, or emergent behavior. In cases where AI is…