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Privacy by Design (PbD) is the most common approach followed by softwaredevelopers who aim to reduce risks within their application designs, yet itremains commonplace for developers to retain little conceptual understanding ofwhat is meant by privacy. A vision is to develop an intelligent privacyassistant to whom developers can easily ask questions in order to learn how toincorporate different privacy-preserving ideas into their IoT applicationdesigns. This paper lays the foundations toward developing such a privacyassistant by synthesising existing PbD knowledge so as to elicit requirements.It is believed that such a privacy assistant should not just prescribe a listof privacy-preserving ideas that developers should incorporate into theirdesign. Instead, it should explain how each prescribed idea helps to protectprivacy in a given application design context-this approach is defined as'Explainable Privacy'. A total of 74 privacy patterns were analysed andreviewed using ten different PbD schemes to understand how each privacy patternis built and how each helps to ensure privacy. Due to page limitations, we havepresented a detailed analysis in [3]. In addition, different real-worldInternet of Things (IoT) use-cases, including a healthcare application, wereused to demonstrate how each privacy pattern could be applied to a givenapplication design. By doing so, several knowledge engineering requirementswere identified that need to be considered when developing a privacy assistant.It was also found that, when compared to other IoT application domains, privacypatterns can significantly benefit healthcare applications. In conclusion, thispaper identifies the research challenges that must be addressed if one wishesto construct an intelligent privacy assistant that can truly augment softwaredevelopers' capabilities at the design phase.

Synthesising Privacy by Design Knowledge Towards Explainable Internet of Things Application Designing in Healthcare