Calibration plays an important role in ensuring device accuracy within safety-critical IoT deployments. The process of calibration involves a number of parties which must collaborate to support calibration. Calibration checks often precede safety-critical operations such as preparing a robot for surgery, requiring inter-party interaction to complete checks. At the same time, the parties involved in a calibration ecosystem may share an adversarial relationship with a subset of other parties. For instance, a surgical robot manufacturer may wish to hide the identities of third-parties from the operator (hospital), in order to maintain confidentiality of business relationships around its robot products. Thus, information flows that reveal who-calibrates-for-whom need to be managed to ensure confidentiality. Similarly, information about what-is-being-calibrated and how-often-it-is-calibrated may compromise operational confidentiality. For example, calibration-verification of connected medical devices may reveal the timing of surgical procedures and compromise PII when combined with other meta information. We show that the challenge of managing information flows between the parties involved in calibration cannot be met by any of the classical access control models, as any one of them or a simple conjunction of a subset such as the lattice model fails to meet the desired access control requirements. We demonstrate that a new unified access control model that combines BIBA, BLP, and Chinese Walls holds rich promise. We study the case for unification, system properties, and develop an XACML-based authorisation framework which enforces the unified model. Upon evaluation against a baseline simple conjunction of the three models individually, our unified model outperforms this, demonstrating it is capable of solving the novel access control challenges thrown up by digital-calibration supply chains.