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

EAGER：SaTC-EDU：人工智能-网络安全交叉培训的隐私增强技术和创新

• 批准号: 2038029
• 负责人: Ling Liu
• 金额: $ 30万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038029&HistoricalAwards=false
• 关键词: EAGER; SaTC; EDU; Privacy; Enhancing

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.

人工智能（AI）正在许多安全关键应用中迅速部署。这推动了人工智能的使用，通过推理和反应的速度来改善网络安全（人工智能网络安全）。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，并强调了对人工智能的鲁棒性和弹性保证（人工智能的网络安全）的需求，同时确保人工智能算法决策的公平性和信任。毫不奇怪，增强隐私的技术和创新对于减轻故意利用和保护人工智能系统的不利影响至关重要。然而，人工智能网络安全交叉培训的资源是有限的，甚至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，该项目将创建一个增强隐私的人工智能网络安全交叉培训试点项目，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市场，并为创建一个安全可靠的人工智能网络安全环境做出贡献，同时支持所有人的人工智能安全、人工智能隐私和人工智能公平。该项目的智力价值源于其首创的研究和教学方法的发展，该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础虚拟实验室（vLab）和三个高级主题虚拟实验室，每个实验室都代表了人工智能网络安全交叉培训的独特教育创新。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的安全性将帮助学生了解隐私是应对各种隐私泄露风险的重要安全保障。人工智能公平与信任虚拟实验室将通过确保所有人的隐私权和人工智能道德，使学生了解隐私是人工智能系统信任和公平的基本衡量标准。通过参与这些vLabs，学生将学习使用风险评估工具来了解人工智能模型攻击的新漏洞，并设计风险缓解工具，以保护人工智能模型学习和推理免受安全或隐私侵犯和算法偏差的影响。该项目由安全与可信网络空间（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前所未有的合作。SaTC项目与《联邦网络安全研究与发展战略计划》和《国家隐私研究战略》保持一致，旨在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。

项目成果

Metric Learning as a Service With Covariance Embedding

• DOI: 10.1109/tsc.2023.3266445
• 发表时间: 2022-11
• 期刊: IEEE Transactions on Services Computing
• 影响因子: 8.1
• 作者: Imam Mustafa Kamal;Hyerim Bae;Ling Liu

Boosting Object Detection Ensembles with Error Diversity

• DOI: 10.1109/icdm54844.2022.00105
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Ka-Ho Chow;Ling Liu

Selecting and Composing Learning Rate Policies for Deep Neural Networks

选择和制定深度神经网络的学习率策略

• DOI: 10.1145/3570508
• 发表时间: 2023
• 期刊: ACM Transactions on Intelligent Systems and Technology
• 影响因子: 5
• 作者: Wu, Yanzhao;Liu, Ling
• 通讯作者: Liu, Ling