Collaborative Research: SaTC: CORE: Small: Secure and Privacy-Preserving Dynamic Spectrum Access

动态频谱共享（DSS）是解决日益严重的无线频谱短缺的关键。DSS系统通常使用称为频谱接入系统（SAS）的自动化频率协调器，其使用专用和众包频谱传感器来实现具有非凡地理覆盖和粒度的按需、经济高效的频谱感测。众包辅助DSS系统长期以来一直面临安全和隐私问题。该项目的创新之处在于TrustSAS的开发、概念原型设计和评估，TrustSAS是一种创新的AI/ML驱动的值得信赖的SAS，它利用专用和众包频谱传感器来确保全面有效的频谱利用率，同时保持强大的安全性和隐私性。该项目更广泛的意义和重要性在于探索AI/ML技术的最新进展，以实现可信赖的DSS，并为AI/ML驱动的无线和移动的安全系统的科学研究进步做出贡献。 该研究包括三个方面的综合研究。Thrust 1开发了VeriSS，这是一个可验证频谱感知的先驱框架，它采用图形神经网络来分析频谱感知数据，并评估其可信度，即使在存在潜在受损频谱传感器的情况下。Thrust 2研究了TrustSML，这是一种基于VeriSS的新型框架，可实现多个同时频谱滥用者的快速，准确和可靠的定位。Thrust 3研究了PriCCS，这是一种新型的空间众包框架，使TrustSAS系统运营商能够执行具有成本效益和准确的频谱感知任务分配，同时为参与人群工作者提供强大的位置隐私保证。该项目的可交付成果，包括教程、讲座、出版物和开放源码工具包，将在网上公开提供。拟议的研究将被纳入课程开发，提高本科生的研究经验，并促进在computing.This奖项的广泛参与反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Dynamic spectrum sharing (DSS) is a crucial solution to the ever-increasing wireless spectrum shortage. DSS systems typically use an automated frequency coordinator called a Spectrum Access System (SAS), which uses both dedicated and crowdsourced spectrum sensors to enable on-demand, cost-effective spectrum sensing with extraordinary geographic coverage and granularity. Crowdsourcing-assisted DSS systems have long faced security and privacy concerns. The project’s novelties are the development, concept prototying, and evaluation of TrustSAS, an innovative AI/ML-powered trustworthy SAS that utilizes both dedicated and crowdsourced spectrum sensors to ensure comprehensive and efficient spectrum utilization while maintaining strong security and privacy. The project's broader significance and importance are the exploration of the latest advances in AI/ML technologies to enable trustworthy DSS and the contribution to the advancement of scientific research in AI/ML-driven wireless and mobile security systems. The proposed research includes three integrated thrusts. Thrust 1 develops VeriSS, a pioneer framework for verifiable spectrum sensing that employs graphical neural networks to analyze spectrum-sensing data and evaluate their trustworthiness even in the presence of potentially compromised spectrum sensors. Thrust 2 investigates TrustSML, a novel framework that builds upon VeriSS to achieve fast, accurate, and trustworthy localization of multiple simultaneous spectrum misusers. Thrust 3 studies PriCCS, a novel spatial crowdsourcing framework that enables the TrustSAS system operator to perform cost-effective and accurate spectrum-sensing task assignment while providing strong location privacy guarantees to participating crowd workers. The project's deliverables, including tutorials, talks, publications, and open-source toolkits, will be made publicly available online. The proposed research will be integrated into curriculum development, enhancing undergraduate research experiences and fostering broad participation in computing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.