Smart healthcare can benefit patients and care providers. However, several challenges exist related to security and privacy. Security solutions have been developed to protect e-health users and medical databases; however, systematic guidelines and implementation are not rolled out in smart healthcare systems. Smart healthcare systems have specific demands when addressing security and privacy issues. From the conceptual perspective, we explored research activities related to authentication, access control, and deidentification and focus especially on key functions and features identified in electronic healthcare systems.

Autonomous systems’ potential to instruct the public during real-life emergencies to foster instantaneous trust and compliance and their impact on rescue operations remain largely unexplored. To determine the requirements for designing technologies capable of delivering instructions in high-risk situations, we needed to understand the key communication elements for establishing immediate trust dynamics, ultimately fostering compliance and contributing to effective life-saving efforts. This paper adopts a participatory approach to curate perspectives from emergency rescue professionals in the UK, gathered through a survey, whose responses were analysed to identify the themes in the dataset and ultimately to elicit verbal and nonverbal elements and message delivery techniques to address the challenges to compliance in interpersonal communication during emergencies. Participants indicated that the adoption of autonomous systems for communication could positively impact rescue operations. They highlighted that verbal communications need to be concise and informative, while nonverbal cues must effectively reinforce verbal messages under distressful conditions. However, challenges such as accountability, adaptability, reliability, and affordability are still prevalent. We formalise a novel communication model designed to engender instantaneous trust between the rescuer and the rescued. We find that verbal elements in the model must increase the situational awareness of the rescued and sufficiently inform them of the context. In contrast, the nonverbal elements should foster credibility, consistency, reliability and positivity between the communicating parties. Based on the professionals’ responses, we further advance recommendations for the use of autonomous systems in emergency rescue scenarios in terms of increasing accountability and accessibility.

Compliance is when a human positively responds to a request or a recommendation given by a system. For example, when prompted, providing your thumbprint for an automated biometric scanner at the airport or starting to watch a new TV show on a streaming service ‘we think you will love’. In trust-related research, compliance is frequently used as a behavioural measure of trust. When evaluating the compliance-trust association in experimental settings, typically, the participants agree, when asked, that they complied because they trusted the system. We developed three scenarios in instantaneous settings where compliance with an instruction delivered by a robot would typically be ascribed to trust. However, rather than asking, ‘Did you trust?’, we asked, ‘Why did you comply?’ In a thematic analysis of responses, we discovered robot design characteristics and sources not related to the design that persuade humans to comply with instructions delivered by a robot.

Personal Carbon Allowances (PCAs) are a policy idea for reducing individual carbon emissions, originally proposed in the UK in the 1990s, but promptly discarded due to concerns about low public acceptability and technological limitations. Decades later, we face the global challenge of a worsened climate crisis, thus proponents of PCAs argue that they should be reconsidered. We conducted an online survey with 300 UK based participants, investigating the viability, trustworthiness, and public acceptance of a Citizen Carbon Budget (CCB) app to monitor and encourage carbon emission reduction from personal activities and the relation of responses to Schwartz’s Portrait Values Questionnaire. Our findings indicate that trust in using this kind of applications should not only be focused on their technical aspects but on the preconditions of trusting the implementation of this policy. Further, we found that holding stronger social values relate to a greater willingness to contribute to minimising individual carbon emissions and consequently to use the app across the board, including greater acceptance of automated features, and willingness to trust the app and stakeholders involved; these were not the case when holding stronger personal values. Various solutions may be needed to appeal to people with different values and leanings for mitigating climate change.

Radical and disruptive interventions are needed to reach "Net Zero" by 2050 to avert the climate catastrophe. Although governments, companies, cities, and institutions have pledged to take action and reduce their carbon emissions, the idea of personal carbon allowances or budgets for individuals has also been proposed as a potential national policy in the UK. In this paper, we employ a Research through Design approach to explore the notion of a carbon budget. We present combined results from two studies: firstly a workshop with members of environmental organisations (industry, charity, and policymaking) discussing the concept of a Citizen Carbon Budget (CCB) and app, from the wide perspective of societal desirability drawn from Responsible Research and Innovation (RRI); and secondly, a one-month deployment of a CCB mobile app with twelve members of the public based in the UK. Key findings from the combination of these approaches showed that the CCB app was fruitful in supporting awareness of personal carbon emissions and reflections about people’s lifestyles. However, several concerns were raised, including the unfairness of treating all people equally in environmental policy, regardless of their background and context. We provide considerations for policymaking and design, including intertwined perspectives drawn from the differing approaches of individual and collective action.

The COVID-19 pandemic has exposed the limitations of current healthcare systems in responding to public health emergencies in a timely and efficient manner. Despite the advantages of digitalized health records, a large portion of health systems are centralized and fall short in guaranteeing basic security and privacy requirements. As a decentralized and distributed technology, blockchain has been utilized for secure data access and information exchange in the healthcare domain. Moreover, COVID-19 health services such as antibody testing, vaccination proofing, contact tracing, health surveillance, and medical supply chains raise higher requirements in transparency, traceability, and immutability. Considering that the COVID-19 pandemic will have a long-term impact on all aspects of society, it is necessary to examine present developments for health information exchange. In this chapter, we discuss emerging platforms that adopt a blockchain architecture, with the focus on responses to the COVID-19 pandemic. Through a literature review, we demonstrate the critical role of blockchain technology in providing a more efficient, trustworthy, secure, and transparent health ecosystem. Finally, we identify future research directions along with recommendations.

INTRODUCTION: Many online services use data-sharing nudges to solicit personal data from their customers for personalized services. OBJECTIVES: This study aims to study people’s privacy preferences in sharing di ff erent types of personal data under di ff erent nudging conditions, how digital nudging can change their data sharing willingness, and if people’s data sharing preferences can be predicted using their responses to a questionnaire. METHODS: This paper reports a machine learning-based analysis on people’s privacy preference patterns under four di ff erent data-sharing nudging conditions (without nudging, monetary incentives, non-monetary incentives, and privacy assurance). The analysis is based on data collected from 685 UK residents who participated in a panel survey. Their self-reported willingness levels towards sharing 23 di ff erent types of personal data were analyzed by using both unsupervised (clustering) and supervised (classification) machine learning algorithms. RESULTS: The results led to a better understanding of people’s privacy preference patterns across di ff erent data-sharing nudging conditions, e.g., our participants’ preferences are distributed in a space of 48 possible profiles more sparsely than we expected, and the unexpected observation that all the three data-sharing nudging strategies led to an overall negative e ff ect: they led to a reduced level of self-reported willingness for more participants, comparing with the case of no nudging at all. Our experiments with supervised machine learning models also showed that people’s privacy (data-sharing) preference profiles can be automatically predicted with a good accuracy, even when a small questionnaire with just seven questions is used. CONCLUSION: Our work revealed a more complicated structure of people’s privacy preference profiles, which have some dependencies on the type of data nudging and the type of personal data shared. Such complicated privacy preference profiles can be e ff ectively analyzed using machine learning methods, including automatic prediction based on a small questionnaire. The negative results on the overall e ff ect of di ff erent data-sharing nudges imply that service providers should consider if and how to use such mechanisms to incentivise their consumers to share personal data. We believe that more consumer-centric and transparent methods and tools should be used to help improve trust between consumers and service providers.

We reflect on our experience in the conceptualization and implementation of Responsible Research and Innovation (RRI) in an exploratory research project funded by the UKRI Trustworthy Autonomous Systems (TAS) Hub. In this paper, we report the narratives captured during a series of focused discussions with the project team and the industry partners using RRI Prompts and Practice Cards (PPC) as a tool to guide the discussion. Inspired by agile software development, we propose an agile-like RRI model for embedding RRI in exploratory research that supplements future growth, while ensuring a human-centred and ethical approach. Such an iterative model promotes continuous RRI focus, sensitivity and consideration for all stakeholders, minimising compromises and risks, whilst fostering team-wide well-being and research validity throughout the research life cycle.

This paper proposes a taxonomy of experimental usecase scenarios to facilitate research into trustworthy autonomous systems (TAS). Unable to identify an open-access repository of usecases to support our research, the project team embarked on development of an online library where fellow researchers would be able to find, share and recommend usecases to other practitioners in the field. To organise the library’s content, we needed a taxonomy and, informed by a commitment to responsible research and innovation (RRI), we prioritised stakeholder involvement to shape its development. Conflict arose, however, between the project team’s objective—a rigorous taxonomy focused on surfacing genuine “benchmarks” that can be used to test a multiplicity of variables in a range of domains under differing experimental conditions—and stakeholder expectation that the library would provide details of particular studies and results. How then can we reconcile project requirements with stakeholder preferences? A practical solution has to be found.

With the rapid development of Industrial 4.0, the modern manufacturing system has been experiencing profoundly digital transformation. The development of new technologies helps to improve the efficiency of production and the quality of products. However, for the increasingly complex production systems, operational decision making encounters more challenges in terms of having sustainable manufacturing to satisfy customers and markets’ rapidly changing demands. Nowadays, rule-based heuristic approaches are widely used for scheduling management in production systems, which, however, significantly depends on the expert domain knowledge. In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. The proximal policy optimization (PPO) algorithm was used in the DRL framework to accelerate the learning process and improve performance. The proposed method was testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art methods.

The drone's open and untrusted environment may create problems for authentication and data sharing. To address this issue, we propose a blockchain-enabled efficient and secure data sharing model for 5G flying drones. In this model, blockchain and attribute-based encryption (ABE) are applied to ensure the security of instruction issues and data sharing. The authentication mechanism in the model employs a smart contract for authentication and access control, public key cryptography for providing accounts and ensuring accounts' security, and a distributed ledger for security audit. In addition, to speed up out-sourced computations and reduce electricity consumption, an ABE model with parallel outsourced computation (ABEM-POC) is constructed, and a generic parallel computation method for ABE is proposed. The analysis of the experimental results shows that parallel computation significantly improves the speed of outsourced encryption and decryption compared to serial computation.

Distributed Internet of Things (Distributed IoT) is a large-scale, heterogeneous, dynamic distributed architecture environment which is gradually formed based on Internet of Things (IoT) technology. In order to cope with the large number access requirements for IoT data brought by application expansion, the data of IoT devices are usually stored in the management server (DMS) of current domain, and adopt a centralized access control mechanism to user. This centrally approach can easily cause data to be tampered with and leaked. Moreover, registering different identities when user accesses different domains increases the difficulty to manage his identities. Therefore, this paper proposes a blockchain-based access control scheme called BacS for Distributed IoT. In BacS, firstly, we use account address of the node in blockchain as the identity to access DMS, redefine the access control permission of data of devices and store on blockchain. Then we design processes of authorization, authorization revocation, access control and audit in BacS. Finally, we use a lightweight symmetric encryption algorithm (SEA) to achieve privacy-preserving for Distributed IoT system. We build a credible experimental model on Ethereum private chain, results show that BacS is feasible and effective that it can achieve secure access in Distributed IoT environment while protecting privacy.

In today's highly connected cyber-physical world, people are constantly disclosing personal and sensitive data to different organizations and other people through the use of online and physical services. Such data disclosure activities can lead to unexpected privacy issues. However, there is a general lack of tools that help to improve users' awareness of such privacy issues and to make more informed decisions on their data disclosure activities in wider contexts. To fill this gap, this paper presents a novel user-centric, data-flow graph based semantic model, which can show how a given user's personal and sensitive data are disclosed to different entities and how different types of privacy issues can emerge from such data disclosure activities. The model enables both manual and automatic analysis of privacy issues, therefore laying the theoretical foundation of building data-driven and user-centric software tools for people to better manage their data disclosure activities in the cyber-physical world.

Although there are many privacy-enhancing tools designed to protect users’ online privacy, it is surprising to see a lack of user-centric solutions allowing privacy control based on the joint assessment of privacy risks and benefits, due to data disclosure to multiple platforms. In this paper, we propose a conceptual framework to fill the gap: aiming at user-centric privacy protection, we show that the framework can assess not only privacy risks in using online services but also the added values earned from data disclosure. Through following a human-in-the-loop approach, it is expected that the framework can provide a personalized solution via preference learning, continuous privacy assessment, behavioral monitoring and nudging. Finally, we describe a case study about “leisure travelers” and some areas for further research.

Digital technologies shape travel environments. Noticing online privacy issues, consumers can hold distinct attitudes towards disclosing personal information to service providers. We conducted a panel survey to gauge travelers’ willingness to share personal information with service providers, provided with different types of nudges. Based on the results of clustering analysis, two segments were identified: travelers who are reasonably willing to share (Privacy Rationalists) and those who are reluctant to share (Privacy Pessimists). This study provides empirical evidence of privacy segmentations in the travel context, which has not been reported before and thus deserves more attention from both researchers and practitioners.

Light-absorbing organic carbon (i.e., brown carbon or BrC) in the atmospheric aerosol has significant contribution to light absorption and radiative forcing. However, the link between BrC optical properties and chemical composition remains poorly constrained. In this study, we combine spectrophotometric measurements and chemical analyses of BrC samples collected from July 2008 to June 2009 in urban Xi'an, Northwest China. Elevated BrC was observed in winter (5 times higher than in summer), largely due to increased emissions from wintertime domestic biomass burning. The light absorption coefficient of methanol-soluble BrC at 365 nm (on average approximately twice that of water-soluble BrC) was found to correlate strongly with both parent polycyclic aromatic hydrocarbons (parent-PAHs, 27 species) and their carbonyl oxygenated derivatives (carbonyl-OPAHs, 15 species) in all seasons ( r2 > 0.61). These measured parent-PAHs and carbonyl-OPAHs account for on average ∼1.7% of the overall absorption of methanol-soluble BrC, about 5 times higher than their mass fraction in total organic carbon (OC, ∼0.35%). The fractional solar absorption by BrC relative to element carbon (EC) in the ultraviolet range (300-400 nm) is significant during winter (42 ± 18% for water-soluble BrC and 76 ± 29% for methanol-soluble BrC), which may greatly affect the radiative balance and tropospheric photochemistry and therefore the climate and air quality.

Record linkage can be used to support current and future health research across populations however such approaches give rise to many challenges related to patient privacy and confidentiality including inference attacks. To address this, we present a semantic-based policy framework where linkage privacy detects attribute associations that can lead to inference disclosure issues. To illustrate the effectiveness of the approach, we present a case study exploring health data combining spatial, ethnicity and language information from several major on-going projects occurring across Australia. Compared with classic access control models, the results show that our proposal outperforms other approaches with regards to effectiveness, reliability and subsequent data utility.