Digital Trust - Trusted Computing and Beyond A Position Paper

Along with the invention of computers and interconnected networks, physical societal notions like security, trust, and privacy entered the digital environment. The concept of digital environments begins with the trust (established in the real world) in the organisation/individual that manages the digital resources. This concept evolved to deal with the rapid growth of the Internet, where it became impractical for entities to have prior offline (real world) trust. The evolution of digital trust took diverse approaches and now trust is defined and understood differently across heterogeneous domains. This paper looks at digital trust from the point of view of security and examines how valid trust approaches from other domains are now making their way into secure computing. The paper also revisits and analyses the Trusted Platform Module (TPM) along with associated technologies and their relevance in the changing landscape. We especially focus on the domains of cloud computing, mobile computing and cyber-physical systems. In addition, the paper also explores our proposals that are competing with and extending the traditional functionality of TPM specifications

Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

An overview of security ontologies

This paper presents an overview of ontologies in Information Systems Security. Information Systems Security is a broad and dynamic area that clearly benefits from the formalizations of concepts provided by ontologies. After a very short presentation of ontologies and Semantic Web, several works in Security Ontologies targeting different aspects of security engineering are presented together with another study that compares several publicly available security ontologies

Semantic privacy-preserving framework for electronic health record linkage

The combination of digitized health information and web-based technologies offers many possibilities for data analysis and business intelligence. In the healthcare and biomedical research domain, applications depending on electronic health records (EHRs) identify privacy preservation as a major concern. Existing solutions cannot always satisfy the evolving research demands such as linking patient records across organizational boundaries due to the potential for patient re-identification. In this work, we show how semantic methods can be applied to support the formulation and enforcement of access control policy whilst ensuring that privacy leakage can be detected and prevented. The work is illustrated through a case study associated with the Australasian Diabetes Data Network (ADDN – www.addn.org.au), the national paediatric type-1 diabetes data registry, and the Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) platform that supports Australia-wide access to urban and built environment data sets. We demonstrate that through extending the eXtensible Access Control Markup Language (XACML) with semantic capabilities, finer-grained access control encompassing data risk disclosure mechanisms can be supported. We discuss the contributions that can be made using this approach to socio-economic development and political management within business systems, and especially those situations where secure data access and data linkage is required

CAPD: A Context-Aware, Policy-Driven Framework for Secure and Resilient IoBT Operations

The Internet of Battlefield Things (IoBT) will advance the operational effectiveness of infantry units. However, this requires autonomous assets such as sensors, drones, combat equipment, and uncrewed vehicles to collaborate, securely share information, and be resilient to adversary attacks in contested multi-domain operations. CAPD addresses this problem by providing a context-aware, policy-driven framework supporting data and knowledge exchange among autonomous entities in a battlespace. We propose an IoBT ontology that facilitates controlled information sharing to enable semantic interoperability between systems. Its key contributions include providing a knowledge graph with a shared semantic schema, integration with background knowledge, efficient mechanisms for enforcing data consistency and drawing inferences, and supporting attribute-based access control. The sensors in the IoBT provide data that create populated knowledge graphs based on the ontology. This paper describes using CAPD to detect and mitigate adversary actions. CAPD enables situational awareness using reasoning over the sensed data and SPARQL queries. For example, adversaries can cause sensor failure or hijacking and disrupt the tactical networks to degrade video surveillance. In such instances, CAPD uses an ontology-based reasoner to see how alternative approaches can still support the mission. Depending on bandwidth availability, the reasoner initiates the creation of a reduced frame rate grayscale video by active transcoding or transmits only still images. This ability to reason over the mission sensed environment and attack context permits the autonomous IoBT system to exhibit resilience in contested conditions

A novel approach to controlled query evaluation in DL-Lite

In Controlled Query Evaluation (CQE) confidential data are protected through a declarative policy and a (optimal) censor, which guarantees that answers to queries are maximized without disclosing secrets. In this paper we consider CQE over Description Logic ontologies and study query answering over all optimal censors. We establish data complexity of the problem for ontologies specified in DL-LiteR and for variants of the censor language, which is the language used by the censor to enforce the policy. In our investigation we also analyze the relationship between CQE and the problem of Consistent Query Answering