EAGER: SaTC-EDU: Instilling a Mindset of Adversarial Thinking into Computer Science Courses Early and Often

EAGER：SaTC-EDU：尽早且经常地将对抗性思维方式灌输到计算机科学课程中

• 批准号: 2039354
• 负责人: Malte Schwarzkopf
• 金额: $ 29.79万
• 依托单位: Brown University
• 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=2039354&HistoricalAwards=false
• 关键词: EAGER; SaTC; EDU; Instilling; Mindset

Security and design flaws in artificial intelligence (AI) algorithms and computer systems can leave our personal information, including sensitive data such as medical records, dangerously exposed, or can give rise to biases that disadvantage or threaten parts of the population. The ability to successfully find these security and design flaws before they cause harm depends on qualified engineers, researchers, and policymakers who understand threats to computer systems and algorithms. However, threat-modeling is typically taught only in advanced Computer Science courses, which come late in the curriculum and which not all students elect to take. This project investigates whether earlier and continued exposure to material on threat modeling and a mindset called "adversarial thinking" improves students' ability to recognize and address challenges in privacy, cybersecurity, and new AI technologies. Adversarial thinking refers to adopting the perspective of an adversary who seeks to exploit weaknesses in a system, algorithm, or model. The resulting course materials and findings will be disseminated, and the findings are expected to motivate changes in the approach to computer science curricula.The project proposes to develop material on adversarial thinking and integrate it into courses at the introductory, intermediate, and advanced level of Brown University’s computer science curriculum. The project team will measure students' performance and progression within each course as well as across courses. The data collected will help answer the project’s central research question: do students who encounter adversarial thinking early in and repeatedly throughout their computer science education show improved ability to recognize and address threats and flaws in computer systems security and AI models? The project will impact academic computer science education through pedagogical methods, skills, and recommendations for curricular structures that help prepare students for the complexities, risks, and opportunities of new technologies.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）算法和计算机系统中的安全和设计缺陷可能会使我们的个人信息（包括医疗记录等敏感数据）暴露在危险之中，或者可能产生偏见，从而损害或威胁部分人群的成功能力。在造成损害之前发现这些安全和设计缺陷取决于了解计算机系统和算法威胁的合格工程师、研究人员和政策制定者。然而，威胁建模通常只在高级计算机科学课程中教授，这些课程出现在课程的后期。并非所有学生都选择参加。该项目调查早期和持续接触有关威胁建模的材料和称为“对抗性思维”的思维方式是否可以提高学生识别和解决隐私、网络安全和新人工智能技术方面的挑战的能力。对抗性思维是指采用对手的视角。旨在利用系统、算法或模型中的弱点的人将传播由此产生的课程材料和研究结果，并且这些研究结果预计将推动计算机科学课程方法的改变。该项目建议开发有关对抗性的材料。思考并将其整合到布朗大学计算机科学课程的入门、中级和高级课程中。项目团队将衡量学生在每门课程以及跨课程中的​​表现和进展。收集的数据将有助于回答该项目的核心问题。研究问题：在计算机科学教育早期和整个过程中反复遇到对抗性思维的学生是否表现出识别和解决计算机系统安全和人工智能模型中的威胁和缺陷的能力有所提高？该项目将通过教学方法、技能、和建议帮助学生为新技术的复杂性、风险和机遇做好准备的课程结构。该项目得到安全可信网络空间 (SaTC) 计划特别倡议的支持，旨在促进网络安全领域之间新的、以前未探索过的合作SaTC 计划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，以保护和维护网络系统不断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 的法定要求。使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。

项目成果

Early Post-Secondary Student Performance of Adversarial Thinking

早期专上学生对抗性思维的表现

• DOI: 10.1145/3446871.3469743
• 发表时间: 2021-01
• 期刊: Proceedings of the 17th ACM Conference on International Computing Education Research
• 作者: Young, Nick;Krishnamurthi, Shriram
• 通讯作者: Krishnamurthi, Shriram

A New Model for Weaving Responsible Computing Into Courses Across the CS Curriculum

将负责任计算融入计算机科学课程的新模式

• DOI: 10.1145/3408877.3432456
• 发表时间: 2021-03
• 期刊: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
• 作者: Cohen, Lena;Precel, Heila;Triedman, Harold;Fisler, Kathi
• 通讯作者: Fisler, Kathi

Hyperspecialized Compilation for Serverless Data Analytics

无服务器数据分析的超专业编译

• 发表时间: 2023-08
• 期刊: Joint Proceedings of Workshops at the 49th International Conference on Very Large Data Bases (VLDB 2023
• 作者: Spiegelberg, Leonhard;Kraska, Tim;Schwarzkopf, Malte
• 通讯作者: Schwarzkopf, Malte

Tuplex: Data Science in Python at Native Code Speed

Tuplex：以本机代码速度使用 Python 进行数据科学

• DOI: 10.1145/3448016.3457244
• 发表时间: 2021-06-09
• 期刊: Proceedings of the 2021 International Conference on Management of Data
• 作者: Leonhard F. Spiegelberg;Rahul Yesantharao;Malte Schwarzkopf;Tim Kraska
• 通讯作者: Tim Kraska

K9db: Privacy-Compliant Storage For Web Applications By Construction

K9db：通过构建实现 Web 应用程序的隐私兼容存储

• 发表时间: 2023-07
• 期刊: Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI
• 作者: Albab, Kinan Dak;Sharma, Ishan;Adam, Justus;Kilimnik, Benjamin;Jeyaraj, Aaron;Paul, Raj;Agvanian, Artem;Spiegelberg, Leonhard;Schwarzkopf, Malte
• 通讯作者: Schwarzkopf, Malte