Human Digital Twins: Efficient Privacy-Preserving Access Control Through Views Pre-materialisation

Digital Twins (DTs) are virtual copies of physical entities, processes, or systems used for various tasks, such as controlling, monitoring, and analysing the status of the real entity. The DT sector is expected to surpass six billion U.S. dollars by 2025, with the Human Digital Twin (HDT) being a prime example. HDTs are being used in various applications, such as personalised medicine, healthcare, and education. However, the materialisation of HDTs can be costly and lead to delays in HDT-based services. To overcome this, we propose a strategy, HDT-ViewMat, to identify the portions of an HDT that should be pre-materialised, considering the trade-off between potential delays and resource waste. The proposed strategy analyses the process/workflow that requires HDT data to estimate the probability of its tasks being executed. Furthermore, due to the sensitivity of the data maintained by the HDTs, access to them must be limited to guarantee the users' privacy. This strategy also considers the compliance of privacy policies with users' preferences. HDT-ViewMat assesses the user's chance of executing a task in the workflow based on the probability of the task's invocation and the probability of the user accepting the policies of the corresponding service provider.