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问题的关键。DSS系统通常使用称为频谱接入系统（SAS）的自动频率协调器，该系统使用专用和众包频谱传感器，实现按需、经济高效的频谱传感，具有非凡的地理覆盖范围和粒度。长期以来，众包辅助的决策支持系统一直面临着安全和隐私问题。该项目的新颖之处在于TrustSAS的开发、概念原型设计和评估，TrustSAS是一种创新的人工智能/机器学习驱动的可信SAS，利用专用和众包频谱传感器确保全面有效的频谱利用，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人工智能/机器学习技术的最新进展，以实现可信赖的决策支持，并为人工智能/机器学习驱动的无线和移动安全系统的科学研究进步做出贡献。拟议的研究包括三个综合的推力。Thrust 1开发了VeriSS，这是一种可验证频谱传感的先驱框架，它采用图形神经网络来分析频谱传感数据，并评估其可信度，即使在存在潜在受损的频谱传感器的情况下。Thrust 2研究TrustSML，这是一种建立在VeriSS基础上的新框架，可实现对多个同时滥用频谱的用户的快速、准确和可信的定位。Thrust 3研究了PriCCS，这是一种新颖的空间众包框架，使TrustSAS系统运营商能够执行成本效益高、准确的频谱传感任务分配，同时为参与众包的工作人员提供强大的位置隐私保障。该项目的可交付成果，包括教程、讲座、出版物和开源工具包，将在网上公开。建议的研究将纳入课程发展，提高本科生的研究经验和促进广泛参与计算机。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.