In recent times, with the advent of blockchain technology, there is an optimism surrounding the concept of self-sovereign identity which is regarded to have influential effect on how we interact with each other over the Internet in future. There are a few works in the literature which examine different aspects of self-sovereign identity. Unfortunately, the existing works are not methodological and comprehensive at all. Moreover, there exist different notions what the term self-sovereign identity means. To exploit its full potential, it is essential to ensure a common understanding in a formal way. This article aims to achieve this goal by providing the first-ever formal and rigorous treatment of the concept self-sovereign identity using a mathematical model. This article examines the properties that a self-sovereign identity should have and explores the impact of self-sovereign identity over the Laws of Identity. It also highlights the essential lifecycles of an Identity Management System and inter-relates how the notion of self-sovereign identity can be applied in these life-cycles. In addition, the article illustrates several envisioned flows involving a selfsovereign identity leveraging blockchain technology covering different aspects of an Identity Management System. All in all, this article presents the first formal and comprehensive step towards an academic investigation of self-sovereign identity.

Distributed Ledger Technology (DLT) has emerged as one of the most disruptive technologies in the last decade. It promises to change the way people do their business, track their products, and manage their personal data. Though the concept of DLT was first implemented in 2009 as Bitcoin, it has gained significant attention only in the past few years. During this time, different DLT enthusiasts and commercial companies have proposed and developed several DLT platforms. These platforms are usually categorized as public vs private, general purpose vs application specific and so on. As a growing number of people are interested to build DLT applications, it is important to understand their underlying architecture and capabilities in order to determine which DLT platform should be leveraged for a specific DLT application. In addition, the platforms need to be evaluated and critically analyzed to assess their applicability, resiliency and sustainability in the long run. In this paper, we have surveyed several leading DLT platforms and evaluated their capabilities based on a number of quantitative and qualitative criteria. The comparative analysis presented in this paper will help the DLT developers and architects to choose the best platform as per their requirement(s).

Digital Twin (DT) is an emerging technology that replicates any physical phenomenon from a physical space to a digital space in congruence with the physical state. However, devising a Healthcare DT model for patient care is seen as a challenging task as the lack of adequate data collection structure. There are also security and privacy concerns as healthcare data is very sensitive and can be used in malicious ways. Because of these current research gaps, the proper way of acquiring the structured data and managing them in a secure way is very important. In this article, we present a mathematical data model to accumulate the patient relevant data in a structured and predefined way with proper delineation. Additionally, the provided data model is described in harmony with real life contexts. Then, we have used the patient centric mathematical data model to formally define the semantic and scope of our proposed Healthcare Digital Twin (HDT) system based on Blockchain. Accordingly, the proposed system is described with all the key components as well as with detailed protocol flows and an analysis of its different aspects. Finally, the feasibility of the proposed model with a critical comparison with other relevant research works have been provided.

Creating and utilizing digital identities are fundamental steps towards accessing online services. In order to facilitate the management of user identities, the concept of identity management has been introduced. Various systems and protocols have been developed to manage online identities. However, these systems are provider-centric, focusing on aiding providers in managing their user bases. As a result, users often have limited control over their identity data and remain unaware of how centralized identity providers use or potentially misuse their data. Self-Sovereign Identity (SSI) has emerged as a new paradigm in the digital identity management landscape, aiming to empower users by allowing them greater control over their identity data. Although SSI is a relatively new domain, there have been numerous efforts, primarily from the industry, to introduce SSI standards, protocols, and systems, with multiple options in each category. Researchers eager to contribute to the SSI domain might find it challenging to understand the interconnections among these components. Notably, the SSI domain faces several challenges, as highlighted in various research works. These challenges must be addressed before SSI can achieve widespread adoption. This article presents a comprehensive systematic literature review of SSI, offers a detailed taxonomy, and identifies and analyzes the open challenges in SSI.

Contact tracing has become a vital tool for public health officials to effectively combat the spread of new diseases, such as the novel coronavirus disease COVID-19. Contact tracing is not new to epidemiologist rather, it used manual or semi-manual approaches that are incredibly time-consuming, costly and inefficient. It mostly relies on human memory while scalability is a significant challenge in tackling pandemics. The unprecedented health and socio-economic impacts led researchers and practitioners around the world to search for technology-based approaches for providing scalable and timely answers. Smartphones and associated digital technologies have the potential to provide a better approach due to their high level of penetration, coupled with mobility. While data-driven solutions are extremely powerful, the fear among citizens is that information like location or proximity associated with other personal data can be weaponised by the states to enforce surveillance. Low adoption rate of such apps due to the lack of trust questioned the efficacy and demanded researchers to find innovative solution for building digital-trust, and appropriately balancing privacy and accuracy of data. In this paper, we have critically reviewed such protocols and apps to identify the strength and weakness of each approach. Finally, we have penned down our recommendations to make the future contact tracing mechanisms more universally inter-operable and privacy-preserving.