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.

Traditional cryptographic techniques have proven to work well on most modern computing devices but they are unsuitable for devices (e.g. IoT devices) where memory, power consumption or processing power is limited. Thus, there has been an increasing amount of work on the design and implementation of lightweight cryptographic algorithms to provide a solution for running cryptography on low resource devices. One particular cryptographic algorithm designed specifically to be used on low resource devices is the PRESENT algorithm. Although the design of PRESENT provides a small memory footprint alongside low power consumption our results show it is susceptible to information leakage when power analysis is performed against a device running this algorithm. In this paper, we present our methodology and results on performing correlation power analysis against this light weight block cipher. Our chosen device under test is an Arduino Uno which was programmed to run the Add Round Key and S-Box functions of PRESENT during the first round of encryptions. Results demonstrate that the Add Round Key function is susceptible to information leakage but a high number of false-positives were observed. Greater success was obtained when targeting the S-Box of the PRESENT algorithm and we were able to derive the first 8 bytes of the key.

Insider threats are a considerable problem within cyber security and it is often difficult to detect these threats using signature detection. Increasing machine learning can provide a solution, but these methods often fail to take into account changes of behaviour of users. This work builds on a published method of detecting insider threats and applies Hidden Markov method on a CERT data set (CERT r4.2) and analyses a number of distance vector methods (Damerau–Levenshtein Distance, Cosine Distance, and Jaccard Distance) in order to detect changes of behaviour, which are shown to have success in determining different insider threats.

Increasingly health records are stored in cloud-based systems, and often protected by a private key. Unfortunately the loss of this key can cause large-scale data loss. This paper outlines a novel Cloud-based architecture (SECRET) which supports keyless encryption methods and which can be used for the storage of patient information, along with supporting failover and a break-glass policy.

The use of digital technologies in providing health care services is collectively known as eHealth. Considerable progress has been made in the development of eHealth services, but concerns over service integration, large scale deployment, and security, integrity and confidentiality of sensitive medical data still need to be addressed. This paper presents a solution proposed by the Data Capture and Auto Identification Reference (DACAR) project to overcoming these challenges. The key contributions of this paper include a Single Point of Contact (SPoC), a novel rule based information sharing policy syntax, and Data Buckets hosted by a scalable and cost-effective Cloud infrastructure. These key components and other system services constitute DACAR's eHealth platform, which allows the secure capture, storage and consumption of sensitive health care data. Currently, a prototype of the DACAR platform has been implemented. To assess the viability and performance of the platform, a demonstration application, namely the Early Warning Score (EWS), has been developed and deployed within a private Cloud infrastructure at Edinburgh Napier University. Simulated experimental results show that the end-to-end communication latency of 97.8% of application messages were below 100ms. Hence, the DACAR platform is efficient enough to support the development and integration of time critical eHealth services. A more comprehensive evaluation of the DACAR platform in a real life clinical environment is under development at Chelsea & Westminster Hospital in London.