With the increased push to promote data-driven methods in modern healthcare, there is a tremendous need for fast access to clinical datasets in order to pursue medical breakthroughs in the areas of personalized medicine and big data knowledge discovery. However, the inherent lack of trust between the data custodians and data consumers/users has resulted in a fully manual honest broker approach to access and process protected healthcare data. Such a manual approach leads to slow data handling, and adds to overheads needed to address data auditability and assurance needed for compliance with healthcare data security standards. In this paper, we address these challenges by proposing a trust model to enable semi-automation of the honest broker process to increase its efficiency. The trust model is based on multi-dimensional risk management principles and considers risk associated with data identifiers, as well as requestor profile and reputation. We implement and evaluate a semi-automated honest broker that uses our trust model in a community cloud testbed using the SynPUF synthetic dataset. Our experiment results show that our multidimensional risk management approach consistently identifies the lower confidentiality risk configuration in the semi-automation in comparison with a one-dimensional strategy. Thus, our semiautomated honest brokering approach improves efficiency for data custodians and data consumers by facilitation of fast and secure data access, while also ensuring compliance in the processing of the protected datasets.