Future-generation healthcare systems will be highly distributed, combining centralised hospital systems with decentralised home-, work-and environment-based monitoring and diagnostics systems. These will reduce costs and injury-related risks whilst both improving quality of service, and reducing the response time for diagnostics and treatments made available to patients. To make this vision possible, medical data must be accessed and shared over a variety of mediums including untrusted networks. In this paper, we present the design and initial implementation of the SERUMS tool-chain for accessing, storing, communicating and analysing highly confidential medical data in a safe, secure and privacy-preserving way. In addition, we describe a data fabrication framework for generating large volumes of synthetic but realistic data, that is used in the design and evaluation of the tool-chain. We demonstrate the present version of our technique on a use case derived from the Edinburgh Cancer Centre, NHS Lothian, where information about the effects of chemotherapy treatments on cancer patients is collected from different distributed databases, analysed and adapted to improve ongoing treatments.

We investigate the performance of a classical constrained optimisation (CCO) algorithm and a constrained optimisation genetic algorithm (GA) for solving the bandwidth allocation for virtual paths (BAVP) problem. We compare throughput, fairness and time complexity of GA-BAVP and CCO-BAVP for several node topologies. The results on maximising the throughput obtained with GA-BAVP and CCO-BAVP are in close agreement, however when considering fairness GA-BAVP outperforms CCO-BAVP, especially for more complex topologies, like the 7-node network, without abundant link capacity. Convergence of the two algorithms appears similar, with GA-BAVP outperforming CCO-BAVP in initial stages, and vice-versa for longer time scales. However as the problem complexity increases the solution time for the genetic algorithm does not increase as fast as the classical constrained optimisation algorithm. A hybrid scheme is also introduced, combining the benefits of both algorithms. It exhibited better overall convergence rate but the same solution as CCO-BAVP.