Privacy policies define the terms under which personal data may be collected and processed by data controllers. The General Data Protection Regulation (GDPR) imposes requirements on these policies that are often difficult to implement. Difficulties arise in particular due to the heterogeneity of existing systems (e.g., the Internet of Things (IoT), web technology, etc.). In this paper, we propose a method to refine high level GDPR privacy requirements for informed consent into low-level computational models. The method is aimed at software developers implementing systems that require consent management. We mechanize our models in \(\text {TLA}^+\) and use model-checking to prove that the low-level computational models implement the high-level privacy requirements; \(\text {TLA}^+\) has been used by software engineers in companies such as Microsoft or Amazon. We demonstrate our method in two real world scenarios: an implementation of cookie banners and a IoT system communicating via Bluetooth low energy.