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: 2244219
• 负责人: Zhipeng Cai
• 金额: $ 23.53万
• 依托单位: Georgia State University Research Foundation, Inc.
• 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=2244219&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.

在隐私敏感和安全关键的应用中，深度学习模型越来越被接受和利用，这种趋势必将持续下去：许多来自在线代码存储库的开源框架和工具都嵌入了深度学习模块。因此，提高作为未来数据工程实践者的大学生的安全意识，并为他们提供设计知识和策略是至关重要的。该项目是值得信赖的、基于深度学习的应用程序。满足了完整性、保密性和公平性（ICE）三个关键领域的迫切需求。一系列易于实施的体验式学习活动具体化了学习者对深度学习模型潜在漏洞的认识，并增强了他们构建安全应用程序的能力。这些活动是专门为先验知识很少的学习者设计的，并且经过简化，以减少教师的准备时间和成本。这些活动的灵活性最大限度地提高了对社会至关重要的相关知识的公平传播。的需求总共十二个学习活动集解决了 ICE 领域出现的各种问题，包括对抗性示例、数据中毒和后门隐藏功能带来的威胁。在学习者配备策略并接受培训以实施适当的防御之前，学习可以让学习者熟悉攻击的过程和影响。为了增强保密性，学习者首先会遇到至少两个潜在的隐私泄露源：数据集过度拟合和滥用。人工智能和深度学习构成了一个快速发展的领域，教育工作者必须跟上步伐，丰富其供给。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。