Collaborative Research: SaTC: EDU: Fire and ICE: Raising Security Awareness through Experiential Learning Activities for Building Trustworthy Deep Learning-based Applications

协作研究：SaTC：EDU：火灾和 ICE：通过体验式学习活动提高安全意识，构建值得信赖的基于深度学习的应用程序

• 批准号: 2244220
• 负责人: Liran Ma
• 金额: $ 22万
• 依托单位: Texas Christian University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244220&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; EDU; Fire

In privacy-sensitive and safety-critical applications, deep learning models are increasingly accepted and utilized. This trend is bound to continue: many open-source frameworks and tools from online code repositories are embedded with deep learning modules. However, many deep learning models contain hidden weaknesses that could be exploited by attacks, posing significant risks to user privacy and safety. It is essential, therefore, to raise security awareness among college students, who are the future data engineering practitioners, and equip them with knowledge and strategies for designing trustworthy, deep learning based applications. This project responds to the urgent need in three critical areas: integrity, confidentiality and equity (ICE). A series of easy-to-implement experiential learning activities concretize learners’ awareness of potential vulnerabilities in deep learning models and enhance their ability to build secure applications of their own. These activities are expressly designed for learners with little prior knowledge, and are streamlined to reduce preparation time and cost for the instructor. The activities’ flexibility maximizes the equitable dissemination of relevant knowledge that is critical to society. The investigators are especially mindful of the needs of minority and socio-economically disadvantaged student populations.A total of twelve learning activity sets address a wide array of issues arising in ICE areas. For data integrity, threats posed by adversarial examples, data poisoning, and backdoor hidden features are tackled. The emphasis on experiential learning allows learners to become acquainted with the process and effects of attacks before learners are equipped with strategies and trained to implement proper defense. To enhance confidentiality, learners first encounter at least two potential sources of privacy leakage, dataset overfitting and abusive querying, and are then taught preventative countermeasures. Both sample biases and algorithmic biases in deep learning models are addressed in the learning activities. Artificial intelligence and deep learning constitute a fast-developing field, and educators must keep pace. The project enriches the supply of educational tools by introducing recent discoveries in the field, including those made by the investigators themselves.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.

在对隐私敏感和关键安全的应用中，深度学习模型越来越被接受和利用。这种趋势必定会继续：许多来自在线代码存储库中的开源框架和工具都嵌入了深度学习模块。但是，许多深度学习模型都包含可能被攻击利用的隐藏弱点，从而对用户隐私和安全构成了重大风险。因此，至关重要的是要提高未来数据工程从业人员的大学生的安全意识，并为他们提供设计基于可信赖的，深度学习的应用程序的知识和策略。该项目应对三个关键领域的迫切需求：诚信，机密性和公平性（ICE）。一系列易于实现的实验学习活动综合了学习者对深度学习模型中潜在脆弱性的认识，并增强了他们建立自己的安全应用程序的能力。这些活动是为知识知识很少的学习者而明确设计的，并且精简以减少教练的准备时间和成本。这些活动的灵活性最大化了对社会至关重要的相关知识的公平传播。调查人员特别注意少数群体和社会经济处于弱势群体的学生人群的需求。总共十二个学习活动集解决了在冰地区引起的各种问题。为了数据完整性，应对对抗性示例的威胁姿势，数据中毒和后门隐藏特征。对经验丰富的学习的重视使学习者可以通过攻击的过程和影响获得，然后学习者配备策略并接受培训以实施适当的防御。为了增强信心，学习者首先遇到了至少两个潜在的隐私泄漏，数据集过度拟合和虐待查询的来源，然后教会了预防性对策。在学习活动中阐述了深度学习模型中的样本偏见和算法偏见。人工智能和深度学习构成了一个快速发展的领域，教育工作者必须保持步伐。该项目通过介绍该领域的最新发现，包括调查人员本身的最新发现，丰富了教育工具的供应。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估来诚实地支持支持。