This work addresses the issues involved in providing robust certification for COVID-19 immunity (assuming the biological premise of 'immunity' is ultimately confirmed). Methods: We developed a prototype mobile phone app and scalable distributed server architecture that facilitates instant verification of tamper-proof test results. Personally identifiable information is only stored at the user's discretion, and the app allows the end-user selectively to present only the specific test result with no other personal information revealed. Behind the scenes it relies upon (a) the 2019 World Wide Web Consortium standard called 'Verifiable Credentials', (b) Tim Berners-Lee's decentralized personal data platform 'Solid', and (c) a consortium Ethereum-based blockchain. Results: Our architecture enables verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalized to avoid restrictive ownership of sensitive digital keys and/or data. Benchmark performance tests show it to scale linearly in the worst case, as significant processing is done locally on each app. For the test certificate Holder, Issuer (e.g. doctor, pharmacy) and Verifier (e.g. employer), it is 'just another app' which takes only minutes to use. Conclusions: The app and distributed server architecture offer a prototype proof of concept that is readily scalable, widely applicable to personal health records and beyond, and in effect 'waiting in the wings' for the biological issues, plus key ethical issues raised in the discussion section, to be resolved.

The need for small and medium enterprises (SMEs) to adopt data analytics has reached a critical point, given the surge of data implied by the advancement of technology. Despite data mining (DM) being widely used in the transportation sector, it is staggering to note that there are minimal research case studies being done on the application of DM by SMEs, specifically in the transportation sector. From the extensive review conducted, the three most common DM models used by large enterprises in the transportation sector are identified, namely “Knowledge Discovery in Database,” “Sample, Explore, Modify, Model and Assess” (SEMMA), and “CRoss Industry Standard Process for Data Mining” (CRISP‐DM). The same finding was revealed in the SMEs' context across the various industries. It was also uncovered that among the three models, CRISP‐DM had been widely applied commercially. However, despite CRISP‐DM being the de facto DM model in practice, a study carried out to assess the strengths and weakness of the models reveals that they have several limitations with respect to SMEs. This paper concludes that there is a critical need for a novel model to be developed in order to cater to the SMEs' prerequisite, especially so in the transportation sector context. This article is categorized under: Application Areas > Business and Industry Application Areas > Industry Specific Applications

Over-centralisation of data leads to tampering and sharing user information without the consent of the owners. This problem has been studied extensively in recent times providing separate solutions involving distributed storage, Blockchain technology and Solid Pods. Individually these solutions are not sufficient to build realistic applications in a decentralised environment; however, a combination of them can effectively provide more powerful and useful use-cases. In this paper, we propose the methods of combining Solid Pods and distributed ledgers in introducing complete decentralisation of data with total user-control, keeping the integrity of the stored information intact through Blockchain-based verification. We demonstrated multiple configurations of our solutions, offering several new use-cases in various sectors. These configurations introduce new dimensions on the Web and mobile applications' data storage that developers can benefit from building Distributed Applications (DApps) in a complete decentralised environment. CCS CONCEPTS • Information systems → Distributed storage; • Security and privacy → Information accountability and usage control; • Social and professional topics → Privacy policies.

Decentralised data solutions bring their own sets of capabilities, requirements and issues not necessarily present in centralised solutions. In order to compare the properties of different approaches or tools for management of decentralised data, it is important to have a common evaluation framework. We present a set of dimensions relevant to data management in decentralised contexts and use them to define principles extending the FAIR framework, initially developed for open research data. By characterising a range of different data solutions or approaches by how TRusted, Autonomous, Distributed and dEcentralised, in addition to how Findable, Accessible, Interoperable and Reusable, they are, we show that our FAIR TRADE framework is useful for describing and evaluating the management of decentralised data solutions, and aim to contribute to the development of best practice in a developing field.