Balancing Trust and Risk in Access Control

The increasing availability of large and diverse datasets (big data) calls for increased flexibility in access control so to improve the exploitation of the data. Risk-aware access control systems offer a natural approach to the problem. We propose a novel access control framework that combines trust with risk and supports access control in dynamic contexts through trust enhancement mechanisms and risk mitigation strategies. This allows to strike a balance between the risk associated with a data request and the trustworthiness of the requester. If the risk is too large compared to the trust level, then the framework can identify adaptive strategies leading to a decrease of the risk (e.g., by removing/obfuscation part of the data through anonymization) or to increase the trust level (e.g., by asking for additional obligation to the requester). We outline a modular architecture to realize our model, and we describe how these strategies can be actually realized in a realistic use case.