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Education DCL: EAGER: Teaching Privacy via Stakeholder Modeling

Education DCL: EAGER: Teaching Privacy via Stakeholder Modeling
教育 DCL:EAGER:通过利益相关者建模教授隐私
批准号:
2335625
负责人:
Malte Schwarzkopf
金额:
$30.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2024
资助国家:
美国
项目状态:
未结题
起止时间:
2024-01-01 至 2025-12-31

项目摘要

项目成果

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中文摘要
翻译
关于隐私和网络安全的社会影响的教育对个人福利和创造符合公共利益的技术至关重要,但要使其有效也是一项挑战。学生们往往很难将隐私权、个人数据控制权或言论自由等抽象概念与他们必须做出的具体技术选择联系起来。该项目实现并评估了一种教授隐私和网络安全的新的教学方法,重点是“利益相关者”:那些直接或间接受到技术系统影响的人。该项目的新颖性在于(1)利益相关者方法,它将关于抽象的高级概念的教学与具体应用相结合,(2)对该方法的有效性进行了初步调查。该项目的更广泛的意义和重要性在于,它为隐私和网络安全创建了一个有效的模板,制作了开放源码的作业材料,并通过新的教学方法和课程建议影响了计算机科学教育。该项目开发了一种新的、以利益相关者为重点的教学隐私和安全的方法。利益攸关方方法是在开发技术系统时对隐私和网络安全关切进行推理的模板,重点是通过利益攸关方的需求将高级别的抽象概念与具体的实施选择联系起来。利益相关者需要为学生提供具体但可能相互冲突的支持要求。理想情况下,利益攸关方做法会导致:(A)学生在与隐私和网络安全有关的抽象概念(如数据自主权和所有权、透明度、问责制)与具体的社会影响(如身份盗窃、名誉损害)之间建立更深层次和更实质性的联系;(B)学生更深入地参与社会影响概念及其与他们所面临的技术决策的关系;以及(C)学生认为网络安全是他们可以努力实现公平和公平计算系统的一个领域。研究团队正在布朗大学的五门计算机科学课程中开发和部署基于利益相关者的作业,这些课程涵盖本科生和网络安全硕士学生学习的入门、中级和高级课程。从这些作业中收集的数据将有助于回答核心的研究问题,即利益相关者方法是否会有效地提高学生将规范概念与具体问题和技术设计决策联系起来的能力。因此,这项工作将通过提出有效教授隐私和网络安全及其社会背景的新教学方法和课程结构,为学术计算机科学教育、劳动力发展和社会责任计算做出贡献。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Education about the social impacts of privacy and cybersecurity is critical to individual welfare and to creating technology that is in the public interest, but it is also challenging to make effective. Students often struggle to relate abstract concepts like the right to privacy, control over personal data, or freedom of speech, to the concrete technical choices they must make. This project realizes and evaluates a new pedagogical approach to teaching privacy and cybersecurity, focused around "stakeholders": people who are directly or indirectly affected by a technical system. The project's novelties are (1) the stakeholder approach, which combines teaching about abstract, high-level concepts with concrete applications, and (2) a preliminary investigation of the effectiveness of this approach. The project's broader significance and importance are that it creates an effective template for privacy and cybersecurity, produces open-source assignment materials, and impacts computer science education through new pedagogies and curricular recommendations.The project develops a new, stakeholder-focused approach to teaching privacy and security. The stakeholder approach is a template for reasoning about privacy and cybersecurity concerns in the development of technical systems, with a focus on relating high-level, abstract concepts to concrete implementation choices via stakeholder needs. The stakeholder needs give students concrete but possibly conflicting requirements to support. Ideally, the stakeholder approach leads to (a) students making deeper and more substantive connections between abstract concepts related to privacy and cybersecurity (e.g., data autonomy and ownership, transparency, accountability) and concrete social impacts (e.g., identity theft, reputational damage); (b) students engaging more deeply with social impact concepts and how they relate to technical decisions they face; and (c) students perceiving cybersecurity as an area in which they can work towards fair and equitable computing systems. The research team is developing and deploying stakeholder-based assignments in five computer science courses at Brown University, spanning introductory, intermediate, and upper-level courses taken by undergraduates and cybersecurity master's students. The data collected from these assignments will help answer the central research question of whether the stakeholder approach results in meaningful improvement in students' ability to relate normative concepts to concrete problems and technical design decisions. This work will therefore contribute to academic computer science education, workforce development, and socially-responsible computing by proposing new pedagogies and curricular structures for effective teaching of privacy and cybersecurity and their societal context.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.
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