Provenance in Agent-mediated Healthcare Systems

Agent-oriented cooperation techniques and standardized electronic healthcare record exchange protocols can be used to combine information regarding different facets of a therapy received by a patient from different healthcare providers at different locations. Provenance is an innovative approach to trace events in complex distributed processes, dependencies between such events, and associated decisions by human actors. We focus on three aspects of provenance in agent-mediated healthcare systems: first, we define the provenance concept and show how it can be applied to agent-mediated healthcare applications; second, we investigate and provide a method for independent and autonomous healthcare agents to document the processes they are involved in without directly interacting with each other; and third, we show that this method solves the privacy issues of provenance in agent-mediated healthcare systems.

Quantified Analysis of the Leakage of Confidential Data

Basic information theory is used to analyse the amount of confidential information which may be leaked by programs written in a very simple imperative language. In particular, a detailed analysis is given of the possible leakage due to equality tests and if statements. The analysis is presented as a set of syntax-directed inference rules and can readily be automated.

Modelling the Provenance of Data in Autonomous Systems

Determining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are textitreactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or textitagents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example.