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 in order to learn how to incorporate different privacy-preserving ideas into their IoT application designs. This paper lays the foundations toward developing such a privacy assistant by synthesising existing PbD knowledge so as 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 [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 paper 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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通过设计知识合成隐私以实现可解释的医疗保健物联网应用程序设计

设计隐私 (PbD) 是旨在降低应用程序设计中风险的软件开发人员所采用的最常见方法，但开发人员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到他们的物联网应用程序设计中。本文通过综合现有的 PbD 知识以获取需求，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该将其纳入其设计的隐私保护想法列表。反而，它应该解释每个规定的想法如何在给定的应用程序设计上下文中帮助保护隐私——这种方法被定义为“可解释的隐私”。使用十种不同的 PbD 方案分析和审查了总共 74 种隐私模式，以了解每种隐私模式的构建方式以及每种模式如何帮助确保隐私。由于篇幅限制，我们在[3]中进行了详细分析。此外，还使用了不同的现实世界物联网 (IoT) 用例（包括医疗保健应用程序）来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益于医疗保健应用。总之，本文确定了如果希望构建能够真正增强软件开发人员在设计阶段的能力的智能隐私助手，则必须解决的研究挑战。