This paper describes prototyping of the draft Solid application interoperability specification (INTEROP). We developed and evaluated a dynamic user interface (UI) for the new Solid application access request and authorization extended with the Data Privacy Vocabulary. Solid places responsibility on users to control their data. INTEROP adds new declarative access controls. Solid applications to date have provided few policy interfaces with high usability. GDPR controls on usage are rarely addressed. Implementation identified specification and Semantic Web tool issues and also in the understandability of declarative policies, a key concern under GDPR or data ethics best practices. The prototype was evaluated in a usability and task accuracy experiment, where the UI enabled users to create access and usage control policies with an accuracy of between 72 and 37%. Overall, the UI had a poor usability rating, with a median SUS (system usability scale) score of 37.67. Experimental participants were classified according to the Westin privacy scale to investigate the impact of user attitudes to privacy on the results. The paper discusses the findings of the study and their consequences for future data sovereignty access request and authorization UI designs.

Data breaches and other security incidents are an emerging challenge in the digital era. The General Data Protection Regulation (GDPR) requires conducting an impact assessment to understand the effects of the breach, and to then notify authorities and affected individuals in certain cases. Communication of this information typically takes place via conventional mediums such as emails and forms on the websites of authorities, and is a manual process. To assist in developing tools to support data breach investigations, and to enable automated systems for assisting with breach assessments and GDPR compliance, we present a machine-readable specification for the representation and documentation of information related to data breaches and their communications. The specification uses current requirements from the GDPR obligations and authoritative guidelines. To represent information, it extends the Data Privacy Vocabulary (DPV) by introducing new concepts required for data breach relevant information.

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide. The major concerns in relation to these apps are around privacy. Ireland was in general praised for the design of its COVID tracker app, and the transparency through which privacy issues were addressed. However, the ”voice” of the Irish public was not really heard or analysed. This study aimed to analyse the Irish public sentiment towards privacy and COVID tracker app. For this purpose we have conducted sentiment analysis on Twitter data collected from public Twitter accounts from Republic of Ireland. We collected COVID-19 related tweets generated in Ireland over a period of time from January 1, 2020 up to December 31, 2020 in order to perform sentiment analysis on this data set. Moreover, the study performed sentiment analysis on the feedback received from a national survey on privacy conducted in Republic of Ireland. The findings of the study reveal a significant criticism towards the app that relate to privacy concerns, but other aspects of the app as well. The findings also reveal some positive attitude towards the fight against COVID-19, but these are not necessarily related to the technological solutions employed for this purpose. The findings of the study contributed to the formulation of useful recommendations communicated to the relevant Irish actors.

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide with major concerns around privacy. COVID Tracker app used in the Republic of Ireland was praised in general for the way it addressed privacy and was used as baseline for other contact tracing apps worldwide. The success of the app is dependent on the general public uptake, hence their voice and attitude is the one that really matters. This paper focuses on developing a survey and the methods aiming to examine the attitudes toward privacy during COVID-19 of the general public in the Republic of Ireland and their impact on the uptake of the COVID tracker app. Various privacy models are used and health belief model as well in this purpose. A pilot study with 286 participants show a change in attitude towards privacy during COVID-19 pandemic, with more people willing to share their data in the interest of saving lives. However, privacy attitudes are shown to have impacted the adoption of the app in Ireland.

Smart city governments face several barriers in adopting open data initiatives. Some of these barriers are related to the attribution rights of open licenses, as reusers often misattribute open data. As a result, publishers do not know which open data is reused and for what, thus ignoring the return on investment and hindering the sustainability of open data initiatives. In addition, reusers who do not fully comply the right of attribution of open data licences may face legal problems. To overcome these pitfalls, this paper envisions an approach that aims to extend open data publication standards with elements coming from open-source software development standards, to support reusers in achieving proper attribution of open data. Our approach is a first attempt to both (i) enabling publishers to gather information to understand open data reuse in smart cities, and (ii) supporting reusers to avoid lawsuit issues related to open data license violation.

This paper presents the Trusted Integrated Knowledge Dataspace (TIKD), a new dataspace, based on linked data technologies and trusted data sharing, that supports integrated knowledge graphs for sensitive application environments such as healthcare. State-of-the-art shared dataspaces do not consider sensitive data and privacy-aware log records as part of their solutions, defining only how to access data. TIKD complements dataspace security approaches through trusted data sharing that considers personal data handling, data privileges, pseudonymization of user activity logging, and privacy-aware data interlinking services. TIKD was implemented on the Access Risk Knowledge (ARK) Platform, a socio-technical risk governance system, and deployed as part of the ARK-Virus Project which aims to govern the risk management of Personal Protection Equipment (PPE) across a group of collaborating healthcare institutions. The ARK Platform was evaluated, both before and after implementing the TIKD, using the ISO 27001 Gap Analysis Tool (GAT) which determines compliance with the information security standard. The results of the evaluation indicated that compliance with ISO 27001 increased from 50% to 85%. The evaluation also provided a set of recommended actions to meet the remaining requirements of the ISO 27001 standard. TIKD provides a collaborative environment, based on knowledge graph integration and GDPR-compliant personal data handling, as part of the data security infrastructure. As a result of this work, a new trusted data security methodology, based on personal data handling, data privileges, access control context specification, and privacy-aware data interlinking, was developed using a knowledge graph approach.

This paper describes a tool using an extended Data Privacy Vocabulary (the DPV) to audit and monitor GDPR compliance of international transfers of personal data. New terms were identified which have been proposed as extensions to the DPV W3C Working Group. A prototype software tool was built based on the model plus a set of validation rules, and synthetic use-cases created to test the capabilities of the model and tool (together a compliance framework). This framework was created because the rules around international transfer compliance are complex and changing, there is an absence of a common approach to ensuring compliance, few tools exist to assist, and those that do lack interoperability. Evaluation results demonstrate that the proposed model improves compliance identification and standardisation. The tool received positive feedback from the data protection practitioners who participated in the evaluation, and an initial version of is now in use in one financial services organisation. While currently the tool only addresses international transfers, in theory the framework can be extended through further work to the broader area of compliance of other aspects of the GPDR.

The creation and maintenance of a Register of Processing Activities (ROPA) is an essential process for the demonstration of GDPR compliance. We analyse ROPA templates from six EU Data Protection Regulators and show that template scope and granularity vary widely between jurisdictions. We then propose a flexible, consolidated data model for consistent processing of ROPAs (CSM-ROPA). We analyse the extent that the Data Privacy Vocabulary (DPV) can be used to express CSM-ROPA. We find that it does not directly address modelling ROPAs, and so needs additional concept definitions. We provide a mapping of our CSM-ROPA to an extension of the Data Privacy Vocabulary.

Typically tools that map non-RDF data into RDF format rely on the technology native to the source of the data when manipulation of data during the mapping is required. Depending on the data format, data manipulation can be performed using underlying technology, such as RDBMS for relational databases or XPath for XML. For CSV/Tabular data there is no such underlying technology, and instead transforming the source data into another format or pre/post-processing techniques are used. As part of this paper, we present a comparison framework for the state-of-the-art in converting CSV/Tabular data into RDF, where a key feature evaluated is transformation functions. We argue that existing approaches for transformation functions in such tools are complex - in number of steps and tools involved - and therefore not as traceable and transparent as one would like. We tackle these problems by defining a more generic, usable and amenable method to incorporate functions into uplift mapping languages, called FunUL. As proof of concept, we show an implementation of our method. Moreover, by using a real world Digital Humanities case study, we compare our approach with other approaches that we have identified to include transformation functions as part of the mapping for CSV/Tabular data.

Linked Open Data consists of a large set of structured data knowledge bases which have been linked together, typically using equivalence statements. These equivalences usually take the form of owl:sameAs statements linking individuals, but links between classes are far less common. Often, the lack of linking between classes is because the relationships cannot be described as elementary one to one equivalences. Instead, complex correspondences referencing multiple entities in logical combinations are often necessary if we want to describe how the classes in one ontology are related to classes in a second ontology. In this paper the authors introduce a novel Bayesian Restriction Class Correspondence Estimation (Bayes-ReCCE) algorithm, an extensional approach to detecting complex correspondences between classes. Bayes-ReCCE operates by analysing features of matched individuals in the knowledge bases, and uses Bayesian inference to search for complex correspondences between the classes these individuals belong to. Bayes-ReCCE is designed to be capable of providing meaningful results even when only small amounts of matched instances are available. They demonstrate this capability empirically, showing that the complex correspondences generated by Bayes-ReCCE have a median F1 score of over 0.75 when compared against a gold standard set of complex correspondences between Linked Open Data knowledge bases covering the geographical and cinema domains. In addition, the authors discuss how metadata produced by Bayes-ReCCE can be included in the correspondences to encourage reuse by allowing users to make more informed decisions on the meaning of the relationship described in the correspondences.

This paper describes a semi-automated process, framework and tools for harvesting, assessing, improving and maintaining high-quality linked-data. The framework, known as DaCura1, provides dataset curators, who may not be knowledge engineers, with tools to collect and curate evolving linked data datasets that maintain quality over time. The framework encompasses a novel process, workflow and architecture. A working implementation has been produced and applied firstly to the publication of an existing social-sciences dataset, then to the harvesting and curation of a related dataset from an unstructured data-source. The framework's performance is evaluated using data quality measures that have been developed to measure existing published datasets. An analysis of the framework against these dimensions demonstrates that it addresses a broad range of real-world data quality concerns. Experimental results quantify the impact of the DaCura process and tools on data quality through an assessment framework and methodology which combines automated and human data quality controls.

When consuming data from federated domains, it is often necessary to identify the relationships that exist between the data schemas used in each domain. Discovering the exact nature of these relationships is difficult due to data set schema heterogeneity. Prior work has focused on inter-domain class equivalence. However it is not always possible to find an equivalent class in both schemas. For example, when instances are modeled as classes in one domain (e.g. router type) but as the attribute values of a single class in the other domain (e.g. router interface). This paper investigates whether when classifying instances in one data set against a second schema, it may be more useful to use some attribute (or attribute group) other than the original class type, to perform this classification. A machine-learning based classification approach to appropriate attribute selection is presented and its operation is evaluated using two large data-sets available on the web as Linked Data. The classification problem is compounded by the less formal semantics of Linked Data when compared to full ontologies but this also highlights the strength of our approach to dealing with noisy or under-specified data-sets and schemas. The experimental results show that our attribute selection approach is capable of discovering appropriate mappings for cases where the correspondence is conditioned on one attribute and that information gain provides a suitable scoring function for selection of correspondence patterns to describe these complex attribute-based mappings.

The heterogeneity of ontologies is a major obstacle to the promised interoperability of knowledge in the Semantic Web. Ontology mappings can help to mitigate the effects by specifying the correspondences between related ontologies. However, the creation of mappings is still complex and time-consuming and therefore it is appealing to discover existing mappings and to reuse them. For reuse, it is essential to understand how a mapping was created and applied. Thus meta-data documenting the lifecycle of an ontology mapping is essential to facilitate management and reuse. Currently the mapping lifecycle is only fragmentary documented and in general the reuse of ontology mappings is insufficiently supported by applications and formats. This paper addresses the question to what extent a semantically expressive ontology-based meta-data model can aid in the discovery, management and reuse of ontology mappings. In particular, experimental results collected from a use-case study on mapping discovery are presented. Based on findings derived, it is shown that semantic rich and effective meta-data describing the ontology mapping lifecycle are crucial for management of ontology mappings. Finally, to address the identified challenges, a framework for management of ontology mappings is outlined.

Federated policy systems are required to support the emergent complexity and organizational heterogeneity of modern Internet service delivery. This paper presents a distributed policy management approach which utilizes a flexible, tree-based capability authority model to partition and delegate federated capabilities or services. A trust management model and a delegation logic is defined which supports secure decentralized policy reasoning and addresses performance overheads due to distributed rule evaluation, threats from malformed or malicious federated principals and allows flexibility with respect to delegation chain reduction or capability authority re-partitioning. The system is evaluated through a security analysis and a prototype implementation of a federated policy engineering framework based on this logic is described. This framework is based on public key certificates and an extension to the Keynote Trust Management language. It provides practical management services such as key discovery and certificate revocation in addition to the core capability delegation function.

Managing a heterogeneous “outer edge” network is complex and error prone. It is typically performed by non-technical users. Effective HAN configuration may be hampered by a poor understanding of HAN service requirements. A challenge is to deploy and maintain meaningful and error-free heterogeneous HAN configurations. This paper explores an integrated solution to address the following requirements: managed capability sharing, usability, and security. A prototype HAN gateway architecture that builds upon explicit user-centric semantics, and enables autonomic management of shared UPnP services with appropriate access controls, is outlined.

Integration of disparate information resources has long been a significant research topic. Semantic approaches can help by allowing expression of concepts divorced from syntax and allowing rich, structured meta-data to be published in a form that is amenable to machine processing, reasoning and inter-domain concept mapping. However the creation of mappings, especially in a dynamic federations of autonomous entities, is time-consuming and vulnerable to brittleness and high maintenance costs due to change at many levels in the system. In this paper we propose an approach to managing change and maximizing mapping reuse by building explicit models of the federal relationship context of mapping deployment. These descriptions enable automated support for mapping reuse suggestions and ease discovery of relevant mappings due to changes at the federation, peer domain, shared capability or local model levels.

Federated policy systems are required to support the complexity and organizational heterogeneity of the modern marketplace. The Community-based Policy Management System (CBPMS) is such a distributed policy management approach. It utilizes a tree-based capability authority model to partition and delegate federated capabilities. However CBPMS delegation chains have limitations such as: performance overheads due to distributed rule evaluation, threats from malformed or malicious federated principals and a lack of flexibility with respect to delegation chain reduction or capability authority re-partitioning. In this paper we introduce a trust management model for CBPMS that addresses all of these issues.. A brief security analysis is presented and a telecommunications service management use case described.

This paper describes work in progress whereby a dynamic data replication scheme, under market-based control is applied to a proposed autonomic distributed data layer for managing configuration management data. The scope of the proposed autonomic system is described and also some experimental work are presented. Analytic approximations of the performance achieved for management requests under various static data replication schemes are compared with event-based simulations of the same system under dynamic market-based replication control. The purpose of this comparison is to evaluate the performance and suitability of a market-based control approach for such autonomic replication systems

This paper describes a model developed to assess data replication strategies employed in distributed operation support systems (OSSs) and network management systems (NMSs) for future public service networks. Maximum network throughput and optimum configuration is determined from network characteristics such as number of network and management nodes, connectivity, relative message processing rates and degree of replication. Practical illustration of the application of this model is demonstrated for a two-layer management network based on management functions for a 3G radio access network.