Digital transformation is a method where new technologies replace the old to meet essential organisational requirements and enhance the end-user experience. Technological transformation often improvises the manner in which a facility or resources are delivered to the recipient. Data is one of the key assets of every organisation which influences significantly reaching the long-term objective. Thus, the entities, as well as technologies involved in the data management process, have a significant role to play to secure different data types. However, the traditional data governance process often follows a centralised approach and thus resulting in various cyber attacks, whereas the distributed approaches are mostly research prototypes and often comprise various security challenges. Security incidents such as data theft fabricate the integrity of confidential data and thus the consequences are often disastrous. To address the challenges, we introduce TRUSTEE, a data-driven platform which aims to provide a secure and privacy-by-design framework to empower companies, organisations, and individuals to access different data domains, use and re-use the data and metadata to extract knowledge with trust and confidentiality. In this paper, we assess the effectiveness of the platform by reviewing the potential challenges and threats associated with the incorporated technologies. Our research emphasises the efficacy of distributed technologies to indicate their significance in data integrity and security.

Advances in quantum computing make Shor’s algorithm for factorising numbers ever more tractable. This threatens the security of any cryptographic system which often relies on the difficulty of factorisation. It also threatens methods based on discrete logarithms, such as with the Diffie-Hellman key exchange method. For a cryptographic system to remain secure against a quantum adversary, we need to build methods based on a hard mathematical problem, which are not susceptible to Shor’s algorithm and create Post Quantum Cryptography (PQC). While high-powered computing devices may be able to run these new methods, we need to investigate how well these methods run on limited powered devices. This paper outlines an evaluation framework for PQC within constrained devices, and contributes to the area by providing benchmarks of the front-running algorithms on a popular single-board low-power device. It also introduces a set of five notions which can be considered to determine the robustness of particular algorithms.

The spread of COVID-19 has highlighted the need for a robust contact tracing infrastructure that enables infected individuals to have their contacts traced, and followed up with a test. The key entities involved within a contact tracing infrastructure may include the Citizen, a Testing Centre (TC), a Health Authority (HA), and a Government Authority (GA). Typically, these different domains need to communicate with each other about an individual. A common approach is when a citizen discloses his personally identifiable information to both the HA a TC, if the test result comes positive, the information is used by the TC to alert the HA. Along with this, there can be other trusted entities that have other key elements of data related to the citizen. However, the existing approaches comprise severe flaws in terms of privacy and security. Additionally, the aforementioned approaches are not transparent and often being questioned for the efficacy of the implementations. In order to overcome the challenges, this paper outlines the PAN-DOMAIN infrastructure that allows for citizen identifiers to be matched amongst the TA, the HA and the GA. PAN-DOMAIN ensures that the citizen can keep control of the mapping between the trusted entities using a trusted converter, and has access to an audit log.