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方案。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来实现按需、经济高效的频谱感知，具有非凡的地理覆盖和粒度。众包辅助的DSS系统长期以来一直面临着安全和隐私方面的担忧。该项目的新颖性是TrustSAS的开发、概念原型和评估，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和高效的频谱利用，同时保持强大的安全性和隐私。该项目的更广泛的意义和重要性是探索AI/ML技术的最新进展，以实现可靠的DSS，并为推动AI/ML驱动的无线和移动安全系统的科学研究做出贡献。拟议的研究包括三个综合推进力。推力1号开发了VeriSS，这是一个可验证频谱感知的先驱框架，它使用图形神经网络来分析频谱感知数据并评估其可信度，即使存在潜在的受损频谱传感器也是如此。推力2研究TrustSML，这是一个建立在VeriSS基础上的新型框架，可实现对多个同时频谱误用用户的快速、准确和可信的定位。推力3研究了PriCCS，这是一个新颖的空间众包框架，使TrustSAS系统运营商能够执行经济高效和准确的频谱感知任务分配，同时为参与的人群工作人员提供强大的位置隐私保证。该项目的成果，包括教程、讲座、出版物和开源工具包，将在网上公开提供。这项拟议的研究将被整合到课程开发中，增强本科生的研究经验，并培养对计算的广泛参与。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.