CPS:SMALL: Privacy-preserving Network Congestion Control: Theory and Applications

该项目的目标是在实现可证明的隐私保障的同时，实现更大程度的共享群众感知的数据。我们的方法是限制对人群感知数据的位置痕迹的知识，以排除来源和目的地，然后使用分布式控制和差异隐私技术来考虑系统：这使得能够探索隐私成本的理论界限。所取得的成果将为建立和分析联网的网络物理系统(CPS)创造一种新的方法，允许用户对人群感知的数据做出决策，同时严格保护他们的隐私。该项目的重点是为学生开发一个软件环境，以探索交通运营中隐私与性能的权衡，以及关于CP的安全和隐私的高级课程。在我们的技术方法中，我们关注用户输入的隐私，如来源(初始状态)、目的地(偏好)和效用函数。这使我们能够建模和分析如何使用群体感知数据来推断这些敏感输入，使用从分布式控制和差异隐私领域改编的技术。各种隐私概念将支持广泛的研究计划，包括专用网络控制的性能限制、针对不同类别的网络物理系统的设计原则，以及考虑位置受欢迎程度的新位置隐私衡量标准。这些贡献推动了概率模型的形式分析领域，以及控制和优化中的隐私这一新兴子领域。理论研究将受到人群来源拥塞检测环境中的模拟和概念验证实施的激励和评估。

The goal of this project is to enable greater sharing of crowd-sensed data, while achieving provable privacy guarantees. Our approach is to limit knowledge of the location traces of crowd-sensed data to exclude the origin and destination, and then consider the system using techniques from distributed control and differential privacy: this enables exploration of the theoretical bounds on the cost of privacy. The achieved results will create a new approach for building and analyzing networked cyber-physical systems (CPS) that permits users to make decisions on crowd-sensed data, and at the same time protects their privacy in a rigorous sense. The project has focused outreach activities in developing a software environment for students to explore privacy-performance trade-offs in transportation operations and an advanced course on security and privacy of CPS.In our technical approach, we focus on the privacy of user inputs, such as the origin (initial state), destination (preference), and utility functions. This enables us to model and analyze how the crowd-sensed data can be used to infer these sensitive inputs, using techniques adapted from the fields of distributed control and differential privacy. The various notions of privacy will support a broad research program, including performance limits of private network control, design principles for different classes of cyber-physical systems, and new location privacy metrics that take into account location popularity. These contributions advance the field of formal analysis of probabilistic models, and the burgeoning subfield of privacy in control and optimization. The theoretical research will be motivated by and evaluated on simulations and proof-of-concept implementations in the context of crowd-sourced congestion detection.