EAGER: SaTC-EDU: Privacy Enhancing Techniques and Innovations for AI-Cybersecurity Cross Training

人工智能(AI)正迅速部署在许多安全关键型应用程序中。这推动了人工智能的使用，通过推理和反应的速度(人工智能用于网络安全)来提高网络安全。与此同时，人工智能的广泛使用给人工智能系统带来了新的对抗性威胁，并突显了对AI(AI的网络安全)的健壮性和弹性保证的必要性，同时确保AI算法决策的公平性和信任。毫不奇怪，隐私增强技术和创新对于减轻故意攻击的不利影响和保护人工智能系统至关重要。然而，用于人工智能-网络安全交叉培训的资源是有限的，更少的项目将与隐私有关的主题、技术和研究创新纳入其涵盖人工智能或网络安全的基础课程。为了弥合这一交叉培训差距并推进人工智能-网络安全教育，该项目将创建一个关于隐私增强的人工智能-网络安全交叉培训的试点计划，将为学生提供变革性的学习体验。该项目的成果将为学生提供人工智能网络安全知识和技能，使他们能够进入劳动力大军，并为创造一个安全可信的人工智能网络安全环境做出贡献，同时支持人工智能安全、人工智能隐私和人工智能公平。该项目的智力价值源于开发了一种首个此类研究和教学方法，将在隐私背景下提供有效的人工智能-网络安全交叉培训。这将包括开发一个隐私基金会虚拟实验室(VLAB)和三个高级主题vLabs，每个都代表着人工智能的独特教育创新-网络安全交叉培训。AI for Security vLab将使学生了解隐私是所有支持AI的网络安全系统和应用程序的关键系统属性。AI VLAB的安全将帮助学生了解隐私是抵御各种隐私泄露风险的重要安全保障。AI公平与信任VLAB将通过确保所有人的隐私权和AI伦理，使学生了解隐私是衡量AI系统信任和公平的基本衡量标准。通过参加这些虚拟实验室，学生们将学习使用风险评估工具来了解AI模型受到攻击的新漏洞，并设计风险缓解工具来保护AI模型的学习和推理免受安全或隐私违规和算法偏见的影响。该项目由安全和值得信赖的网络空间(SATC)计划的一项特别倡议支持，旨在促进网络安全、人工智能和教育领域之间的新的、以前未探索的合作。SATC计划与联邦网络安全研究和发展战略计划和国家隐私研究战略保持一致，以保护和维护网络系统日益增长的社会和经济效益，同时确保安全和隐私。该奖项反映了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。

Artificial intelligence (AI) is being rapidly deployed in many security-critical applications. This has fueled the use of AI to improve cybersecurity via speed of reasoning and reaction (AI for cybersecurity). At the same time, the widespread use of AI introduces new adversarial threats to AI systems and highlights a need for robustness and resilience guarantees for AI (cybersecurity for AI), while ensuring fairness of and trust in AI algorithmic decision making. Not surprisingly, privacy-enhancing technologies and innovations are critical to mitigating the adverse effects of intentional exploitation and protecting AI systems. However, resources for AI-cybersecurity cross-training are limited, and even fewer programs integrate topics, techniques and research innovations pertaining to privacy in their basic curricula covering AI or cybersecurity. To bridge this cross-training gap and to advance AI-cybersecurity education, this project will create a pilot program on privacy-enhancing AI-cybersecurity cross-training, which will provide a transformative learning experience for students. The results of this project will provide students with the AI-cybersecurity knowledge and skills that will enable them to enter the workforce and contribute to the creation of a secure and trustworthy AI-cybersecurity environment that simultaneously supports AI safety, AI privacy and AI fairness for all. The intellectual merit of this project stems from the development of a first-of-its-kind research and teaching methodology that will provide effective AI-cybersecurity cross-training in the context of privacy. This will include developing a privacy foundation virtual laboratory (vLab) and three advanced topic vLabs, each representing a unique educational innovation for AI-cybersecurity cross-training. The AI for Security vLab will enable students to learn that privacy is a critical system property for all AI-enabled cybersecurity systems and applications. The Security of AI vLab will assist students in learning that privacy is an important safety guarantee against a variety of privacy leakage risks. The AI Fairness and Trust vLab will empower students to learn that privacy is an essential measure of trust and fairness of AI systems by ensuring the right to privacy and AI ethics for all. By participating in these vLabs, students will learn to use risk assessment tools to understand new vulnerabilities to attack of AI models and to design risk-mitigation tools to protect AI model learning and reasoning against security or privacy violations and algorithmic biases.This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.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.