脑机接口应用中保护隐私和网络安全的框架
A Framework for Preserving Privacy and Cybersecurity in Brain-Computer Interfacing Applications
摘要：
脑机接口（BCI）包括一个快速发展的技术领域，其在从医学到工业到艺术、游戏和军事的领域中具有深远影响的潜力。如今，这些新兴的BCI应用通常仍处于早期技术准备阶段，但由于BCI为人脑创造了新颖的技术通信渠道，它们引发了隐私和安全问题。为了减轻这种风险，文献中已经提出了大量的对策，但是缺乏描述如何通过设计来保护BCI应用的隐私和安全性的一般框架，即，它已经作为早期BCI设计过程的一个组成部分，以系统的方式，并允许对不同的背景进行适当的深度分析，例如商业BCI产品开发与学术研究和实验室原型。在这里，我们建议采用最近的系统工程方法的隐私威胁建模，风险评估，和隐私工程的BCI领域。这些方法以比以前的方法更系统和更全面的方式解决隐私和安全问题，并提供有关如何从原则转向行动的可重用模式。我们将这些方法应用于BCI和数据流，并推导出一个通用的，可扩展的，可操作的脑隐私保护的BCI应用程序中的网络安全框架。该框架旨在灵活应用于当前和未来的广泛BCI应用。我们还提出了一系列新颖的隐私设计功能的BCI，重点是功能促进BCI的透明度作为一个先决条件的BCI用户的信息自决，以及设计功能，以确保BCI用户的自主性。我们预计，我们的框架将有助于发展尊重隐私，值得信赖的BCI技术。
英文摘要：
Brain-Computer Interfaces (BCIs) comprise a rapidly evolving field of technology with the potential of far-reaching impact in domains ranging from medical over industrial to artistic, gaming, and military. Today, these emerging BCI applications are typically still at early technology readiness levels, but because BCIs create novel, technical communication channels for the human brain, they have raised privacy and security concerns. To mitigate such risks, a large body of countermeasures has been proposed in the literature, but a general framework is lacking which would describe how privacy and security of BCI applications can be protected by design, i.e., already as an integral part of the early BCI design process, in a systematic manner, and allowing suitable depth of analysis for different contexts such as commercial BCI product development vs. academic research and lab prototypes. Here we propose the adoption of recent systems-engineering methodologies for privacy threat modeling, risk assessment, and privacy engineering to the BCI field. These methodologies address privacy and security concerns in a more systematic and holistic way than previous approaches, and provide reusable patterns on how to move from principles to actions. We apply these methodologies to BCI and data flows and derive a generic, extensible, and actionable framework for brain-privacy-preserving cybersecurity in BCI applications. This framework is designed for flexible application to the wide range of current and future BCI applications. We also propose a range of novel privacy-by-design features for BCIs, with an emphasis on features promoting BCI transparency as a prerequisite for informational self-determination of BCI users, as well as design features for ensuring BCI user autonomy. We anticipate that our framework will contribute to the development of privacy-respecting, trustworthy BCI technologies.
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