GlucOS：自动化胰岛素输送的安全性、正确性与简洁性
GlucOS: Security, correctness, and simplicity for automated insulin delivery
AI总结：
GlucOS是一个用于自动化胰岛素输送的可信系统，通过新颖架构和整体安全机制，实现个性化剂量管理并抵御恶意模型，实际部署与模拟验证了其安全性和血糖控制改善效果。
摘要：
我们提出了GlucOS，一个用于可信自动化胰岛素输送的新型系统。从根本上说，本文介绍的是我们设计、实现并在真实人类身上部署的一个系统，以及从我们的经验中汲取的教训。GlucOS引入了一种新颖的架构，允许用户使用任何预测模型（包括机器学习）进行胰岛素剂量个性化管理，同时保护他们免受恶意模型的侵害。我们还引入了一种新颖的整体安全机制，能够适应人类生理学上前所未有的变化。我们使用形式化方法证明关键组件的正确性，并将人类纳入我们的防御策略。我们的评估包括在七名个体上的实际部署以及模拟结果，以表明我们的技术具有泛化能力。我们强调，我们的结果并非来自实验室研究，而是人们在日常生活中使用GlucOS管理1型糖尿病。我们的结果表明，即使在攻击条件下，GlucOS也能保持安全性并改善血糖控制。这项工作展示了安全、个性化、自动化医疗保健系统的潜力。我们的全部源代码可在该链接获取。
英文摘要：
We present GlucOS, a novel system for trustworthy automated insulin delivery. Fundamentally, this paper is about a system we designed, implemented, and deployed on real humans and the lessons learned from our experiences. GlucOS introduces a novel architecture that allows users to personalize diabetes management using any predictive model (including ML) for insulin dosing while simultaneously protecting them against malicious models. We also introduce a novel holistic security mechanism that adapts to unprecedented changes to human physiology. We use formal methods to prove correctness of critical components and incorporate humans as part of our defensive strategy. Our evaluation includes both a real-world deployment with seven individuals and results from simulation to show that our techniques generalize. We highlight that our results are not from a lab study, with people using GlucOS to manage Type 1 Diabetes in their daily lives. Our results show that GlucOS maintains safety and improves glucose control even under attack conditions. This work demonstrates the potential for secure, personalized, automated healthcare systems. Our entire source code is available at this link.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
