Secure Data Hiding for Contact Tracing

Contact tracing is an effective tool in controlling the spread of infectious diseases such as COVID-19. It involves digital monitoring and recording of physical proximity between people over time with a central and trusted authority, so that when one user reports infection, it is possible to identify all other users who have been in close proximity to that person during a relevant time period in the past and alert them. One way to achieve this involves recording on the server the locations, e.g. by reading and reporting the GPS coordinates of a smartphone, of all users over time. Despite its simplicity, privacy concerns have prevented widespread adoption of this method. Technology that would enable the "hiding" of data could go a long way towards alleviating privacy concerns and enable contact tracing at a very large scale. In this article we describe a general method to hide data. By hiding, we mean that instead of disclosing a data value x, we would disclose an "encoded" version of x, namely E(x), where E(x) is easy to compute but very difficult, from a computational point of view, to invert. We propose a general construction of such a function E and show that it guarantees perfect recall, namely, all individuals who have potentially been exposed to infection are alerted, at the price of an infinitesimal number of false alarms, namely, only a negligible number of individuals who have not actually been exposed will be wrongly informed that they have

Secure Data Hiding for Contact Tracing

Contact tracing is an effective tool in controlling the spread of infectious diseases such as COVID-19. It involves digital monitoring and recording of physical proximity between people over time with a central and trusted authority, so that when one user reports infection, it is possible to identify all other users who have been in close proximity to that person during a relevant time period in the past and alert them. One way to achieve this involves recording on the server the locations, e.g. by reading and reporting the GPS coordinates of a smartphone, of all users over time. Despite its simplicity, privacy concerns have prevented widespread adoption of this method. Technology that would enable the hiding of data could go a long way towards alleviating privacy concerns and enable contact tracing at a very large scale. In this article we describe a general method to hide data. By hiding, we mean that instead of disclosing a data value x, we would disclose an encoded version of x, namely E(x), where E(x) is easy to compute but very difficult, from a computational point of view, to invert. We propose a general construction of such a function E and show that it guarantees perfect recall, namely, all individuals who have potentially been exposed to infection are alerted, at the price of an infinitesimal number of false alarms, namely, only a negligible number of individuals who have not actually been exposed will be wrongly informed that they have

Intelligent Security Provisioning and Trust Management for Future Wireless Communications

The fifth-generation (5G)-and-beyond networks will provide broadband access to a massive number of heterogeneous devices with complex interconnections to support a wide variety of vertical Internet-of-Things (IoT) applications. Any potential security risk in such complex systems could lead to catastrophic consequences and even system failure of critical infrastructures, particularly for applications relying on tight collaborations among distributed devices and facilities. While security is the cornerstone for such applications, trust among entities and information privacy are becoming increasingly important. To effectively support future IoT systems in vertical industry applications, security, trust and privacy should be dealt with integratively due to their close interactions. However, conventional technologies always treat these aspects separately, leading to tremendous security loopholes and low efficiency. Existing solutions often feature various distinctive weaknesses, including drastically increased latencies, communication and computation overheads, as well as privacy leakage, which are extremely undesirable for delay-sensitive, resource-constrained, and privacy-aware communications. To overcome these issues, this thesis aims at creating new multi-dimensional intelligent security provisioning and trust management approaches by leveraging the most recent advancements in artificial intelligence (AI). The performance of the existing physical-layer authentication could be severely affected by the imperfect estimate and the variation of physical link attributes, especially when only a single attribute is employed. To overcome this challenge, two multi-dimensional adaptive schemes are proposed as intelligent processes to learn and track the all available physical attributes, hence to improve the reliability and robustness of authentication by fusing multiple attributes. To mitigate the effects of false authentication, an adaptive trust management-based soft authentication and progressive authorization scheme is proposed by establishing trust between transceivers. The devices are authorized by their trust values, which are dynamically evaluated in real-time based on the varying attributes, resulting in soft security and progressive authorization. By jointly considering security and privacy-preservation, a distributed accountable recommendation-based access scheme is proposed for blockchain-enabled IoT systems. Authorized devices are introduced as referrers for collaborative authentication, and the anonymous credential algorithm helps to protect privacy. Wrong recommendations will decrease the referrers’ reputations, named as accountability. Finally, to secure resource-constrained communications, a lightweight continuous authentication scheme is developed to identify devices via their pre-arranged pseudo-random access sequences. A device will be authenticated as legitimate if its access sequences are identical to the pre-agreed unique order between the transceiver pair, without incurring long latency and high overhead. Applications enabled by 5G-and-beyond networks are expected to play critical roles in the coming connected society. By exploring new AI techniques, this thesis jointly considers the requirements and challenges of security, trust, and privacy provisioning, and develops multi-dimensional intelligent continuous processes for ever-growing demands of the quality of service in diverse applications. These novel approaches provide highly efficient, reliable, model-independent, situation-aware, and continuous protection for legitimate communications, especially in the complex time-varying environment under unpredictable network dynamics. Furthermore, the proposed soft security enables flexible designs for heterogeneous IoT devices, and the collaborative schemes provide efficient solutions for massively distributed entities, which are of paramount importance to diverse industrial applications due to their ongoing convergence with 5G-and-beyond networks

Constitutional Challenges in the Algorithmic Society

The law struggles to address the constitutional challenges of the algorithmic society. This book is for scholars and lawyers interested in the intersections of law and technology. It addresses the challenges for fundamental rights and democracy, the role of policy and regulation, and the responsibilities of private actors

A decentralised secure and privacy-preserving e-government system

Electronic Government (e-Government) digitises and innovates public services to businesses, citizens, agencies, employees and other shareholders by utilising Information and Communication Technologies. E-government systems inevitably involves finance, personal, security and other sensitive information, and therefore become the target of cyber attacks through various means, such as malware, spyware, virus, denial of service attacks (DoS), and distributed DoS (DDoS). Despite the protection measures, such as authentication, authorisation, encryption, and firewalls, existing e-Government systems such as websites and electronic identity management systems (eIDs) often face potential privacy issues, security vulnerabilities and suffer from single point of failure due to centralised services. This is getting more challenging along with the dramatically increasing users and usage of e-Government systems due to the proliferation of technologies such as smart cities, internet of things (IoTs), cloud computing and interconnected networks. Thus, there is a need of developing a decentralised secure e-Government system equipped with anomaly detection to enforce system reliability, security and privacy. This PhD work develops a decentralised secure and privacy-preserving e-Government system by innovatively using blockchain technology. Blockchain technology enables the implementation of highly secure and privacy preserving decentralised applications where information is not under the control of any centralised third party. The developed secure and decentralised e-Government system is based on the consortium type of blockchain technology, which is a semi-public and decentralised blockchain system consisting of a group of pre-selected entities or organisations in charge of consensus and decisions making for the benefit of the whole network of peers. Ethereum blockchain solution was used in this project to simulate and validate the proposed system since it is open source and supports off-chain data storage such as images, PDFs, DOCs, contracts, and other files that are too large to be stored in the blockchain or that are required to be deleted or changed in the future, which are essential part of e-Government systems. This PhD work also develops an intrusion detection system (IDS) based on the Dendritic cell algorithm (DCA) for detecting unwanted internal and external traffics to support the proposed blockchain-based e-Government system, because the blockchain database is append-only and immutable. The IDS effectively prevent unwanted transactions such as virus, malware or spyware from being added to the blockchain-based e-Government network. Briefly, the DCA is a class of artificial immune systems (AIS) which was introduce for anomaly detection in computer networks and has beneficial properties such as self-organisation, scalability, decentralised control and adaptability. Three significant improvements have been implemented for DCA-based IDS. Firstly, a new parameters optimisation approach for the DCA is implemented by using the Genetic algorithm (GA). Secondly, fuzzy inference systems approach is developed to solve nonlinear relationship that exist between features during the pre processing stage of the DCA so as to further enhance its anomaly detection performance in e-Government systems. In addition, a multiclass DCA capable of detection multiple attacks is developed in this project, given that the original DCA is a binary classifier and many practical classification problems including computer network intrusion detection datasets are often associated with multiple classes. The effectiveness of the proposed approaches in enforcing security and privacy in e- Government systems are demonstrated through three real-world applications: privacy and integrity protection of information in e Government systems, internal threats detection, and external threats detection. Privacy and integrity protection of information in the proposed e- Government systems is provided by using encryption and validation mechanism offered by the blockchain technology. Experiments demonstrated the performance of the proposed system, and thus its suitability in enhancing security and privacy of information in e-Government systems. The applicability and performance of the DCA-based IDS in e Government systems were examined by using publicly accessible insider and external threat datasets with real world attacks. The results show that, the proposed system can mitigate insider and external threats in e-Government systems whilst simultaneously preserving information security and privacy. The proposed system also could potentially increase the trust and accountability of public sectors due to the transparency and efficiency which are offered by the blockchain applications

Alternative Finance Strategies for Small Business Sustainability and Growth

Many small business leaders lack alternative financing strategies to sustain and grow their businesses. Small business leaders are concerned with accessing financial capital to ensure sustainability. Grounded in Donaldson’s pecking order theory (POT), the purpose of this qualitative multiple case study was to explore alternative finance strategies some small business leaders use to sustain and grow their businesses. Participants comprised five small business leaders in Oakland, California, with successful experiences using alternative financing strategies to raise financial capital for their businesses. Data were collected from semistructured interviews, archival organizational documentation, and physical artifacts. Yin’s 5-step analysis process guided the data analysis. The following themes emerged: financing strategies of small business leaders, modification strategies used to improve financial effectiveness, strategies for overcoming financial constraints, and strategies to minimize the effects of the COVID-19 health crisis on small business sustainability. A key recommendation is for small business leaders to maintain accurate financial records to monitor the performance of their businesses. By improving their record-keeping systems, small business leaders may reduce costly consequences and promote financial sustainability. Implications for positive social change include the potential for business leaders to increase their ability to implement alternative finance strategies to generate revenues. Higher revenues may lead to more economic growth that entrepreneurs could use to create jobs in their local communities

Identity and identification in an information society: Augmenting formal systems of identification with technological artefacts

Information and Communication Technology (ICT) are transforming society’s information flows. These new interactive environments decouple agents, information and actions from their original contexts and this introduces challenges when evaluating trustworthiness and intelligently placing trust.This thesis develops methods that can extend institutional trust into digitally enhanced interactive settings. By applying privacy-preserving cryptographic protocols within a technical architecture, this thesis demonstrates how existing human systems of identification that support institutional trust can be augmented with ICT in ways that distribute trust, respect privacy and limit the potential for abuse. Importantly, identification systems are located within a sociologically informed framework of interaction where identity is more than a collection of static attributes.A synthesis of the evolution and systematisation of cryptographic knowledge is presented and this is juxtaposed against the ideas developed within the digital identity community. The credential mechanism, first conceptualised by David Chaum, has matured into a number of well specified mathematical protocols. This thesis focuses on CL-RSA and BBS+, which are both signature schemes with efficient protocols that can instantiate a credential mechanism with strong privacy-preserving properties.The processes of managing the identification of healthcare professionals as they navigate their careers within the Scottish Healthcare Ecosystem provide a concrete case study for this work. The proposed architecture mediates the exchange of verifiable, integrity-assured evidence that has been cryptographically signed by relevant healthcare institutions, but is stored, managed and presented by the healthcare professionals to whom the evidence pertains.An evaluation of the integrity-assured transaction data produced by this architecture demonstrates how it could be integrated into digitally augmented identification processes, increasing the assurance that can be placed in these processes. The technical architecture is shown to be practical through a series of experiments run under realistic production-like settings.This work demonstrates that designing decentralised, standards-based, privacy-preserving identification systems for trusted professionals within highly assured social contexts can distribute institutionalised trust to trustworthy individuals and empower these individuals to interface with society’s increasingly socio-technical systems

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems