Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is meant by privacy. A vision is to develop an intelligent privacy assistant to whom developers can easily ask questions to learn how to incorporate different privacy-preserving ideas into their IoT application designs. This article lays the foundations toward developing such a privacy assistant by synthesising existing PbD knowledge to elicit requirements. It is believed that such a privacy assistant should not just prescribe a list of privacy-preserving ideas that developers should incorporate into their design. Instead, it should explain how each prescribed idea helps to protect privacy in a given application design context—this approach is defined as “Explainable Privacy.” A total of 74 privacy patterns were analysed and reviewed using ten different PbD schemes to understand how each privacy pattern is built and how each helps to ensure privacy. Due to page limitations, we have presented a detailed analysis in Reference [3]. In addition, different real-world Internet of Things (IoT) use-cases, including a healthcare application, were used to demonstrate how each privacy pattern could be applied to a given application design. By doing so, several knowledge engineering requirements were identified that need to be considered when developing a privacy assistant. It was also found that, when compared to other IoT application domains, privacy patterns can significantly benefit healthcare applications. In conclusion, this article identifies the research challenges that must be addressed if one wishes to construct an intelligent privacy assistant that can truly augment software developers’ capabilities at the design phase.

中文翻译：

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通过设计知识综合隐私，实现医疗保健中可解释的物联网应用设计

设计隐私 (PbD) 是软件开发人员最常用的方法，旨在降低其应用程序设计中的风险，但对于开发人员来说，对隐私的含义几乎没有概念性理解仍然司空见惯。一个愿景是开发一个智能隐私助手，开发人员可以轻松地向其提问，以了解如何将不同的隐私保护理念融入他们的物联网应用程序设计中。本文通过综合现有的 PbD 知识来引出需求，为开发此类隐私助手奠定了基础。人们认为，这样的隐私助手不应该只是规定开发人员应将其纳入其设计的隐私保护想法列表。反而，“可解释的隐私。”使用十种不同的 PbD 方案对总共 74 种隐私模式进行了分析和审查，以了解每种隐私模式是如何构建的，以及每种隐私模式如何帮助确保隐私。由于篇幅限制，我们在参考文献[3]中给出了详细的分析。此外，不同的现实世界物联网 (IoT) 用例（包括医疗保健应用程序）用于演示如何将每种隐私模式应用于给定的应用程序设计。通过这样做，确定了在开发隐私助手时需要考虑的几个知识工程要求。还发现，与其他物联网应用领域相比，隐私模式可以显着有利于医疗保健应用。综上所述，