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

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使用。这种趋势必然会持续下去：来自在线代码存储库的许多开源框架和工具都嵌入了深度学习模块。然而，许多深度学习模型都包含可能被攻击利用的隐藏弱点，对用户隐私和安全构成重大风险。因此，提高作为未来数据工程实践者的大学生的安全意识，并为他们提供设计值得信赖的、基于深度学习的应用程序的知识和策略是至关重要的。该项目满足了三个关键领域的迫切需求：诚信、保密和公平 (ICE)。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中潜在漏洞的认识，并增强了他们构建自己的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。调查人员特别关注少数族裔和社会经济弱势学生群体的需求。总共 12 个学习活动集解决了 ICE 领域出现的各种问题。为了数据完整性，解决了对抗性示例、数据中毒和后门隐藏功能带来的威胁。对体验式学习的强调使学习者能够熟悉攻击的过程和影响，然后再配备策略并接受培训以实施适当的防御。为了增强保密性，学习者首先遇到至少两个潜在的隐私泄露源：数据集过度拟合和滥用查询，然后学习预防对策。深度学习模型中的样本偏差和算法偏差都在学习活动中得到解决。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。该项目通过介绍该领域的最新发现（包括研究人员自己的发现）丰富了教育工具的供应。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。

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.