CHS: Small: Multidisciplinary Design Approaches for Privacy and Security

数字设备和日常物品日益变得智能、互联和人工智能，尽管这带来了许多好处，但也引发了新的隐私问题并引入了新的安全漏洞。 这些担忧和脆弱性是广泛的，尽管它们对人们的影响因人的背景、身份和环境而异。例如，健身追踪器、声控智能音箱、面部识别智能摄像头等智能设备可用于监控家庭成员、与第三方共享个人健康数据、监视保姆和护理人员、骚扰和虐待亲密伴侣、诱骗用户共享密码或泄露秘密。 该项目将开发新颖的原型和策略，以解决对使用它们的人和环境敏感的智能设备引起的隐私和安全问题。该项目的主要新颖之处在于其多学科方法，将数字隐私和安全方面的专业知识与交互、工业、图形和建筑设计等设计学科的专业知识相结合。通过让不同的非专家和专家参与原型解决方案的设计、开发和评估，该项目将扩大对隐私和安全研究和教育的参与，以生成对每个人都具有包容性、适当、公平和有效的解决方案和策略。该项目的一个主要目标是寻找目前推动大多数面向用户的隐私和安全研究的知情同意和可用选择方法的替代方案。 这些方法常常会失败：隐私政策经常被忽视或难以理解，即使写得很清楚，实际上也会降低信任，而基于选择的方法往往无法适应根据经验、个性、身份、能力和情况而有很大差异的隐私决策和偏好。 为了解决这些问题，研究团队将进行定性实地考察和参与式设计研究，邀请不同的非专家和专家参与。 这种多学科方法旨在通过以下方式为隐私、安全和以人为中心的计算研究做出贡献：(1) 生成新颖的多种形式、社会敏感且适合情境的解决方案，以解决当前和未来的隐私和安全问题，特别是那些与互联、人工智能和“永远在线”日常技术扩散相关的问题，(2) 扩大和多样化对隐私和安全问题框架、解决方案寻找和讨论的参与，(3) 开发前瞻性方法、策略和工程、计算机科学、设计、政策制定以及其他专家和专业人士预测和应对未来隐私和安全问题的示例原型。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。

Digital devices and everyday objects are increasingly becoming smart, connected, and artificially intelligent, and though this promises numerous benefits, it also raises new privacy concerns and introduces new security vulnerabilities. These concerns and vulnerabilities are wide-ranging, although they affect people differently depending on someone's background, identity, and context. For example, smart devices such as fitness trackers, voice controlled smart speakers, and facial recognition smart cameras can be used to monitor family members, share personal health data with third parties, surveil nannies and caregivers, harass and abuse intimate partners, and trick users into sharing passwords or divulging secrets. This project will develop novel prototypes and strategies for addressing privacy and security concerns arising from smart devices that are sensitive to the people and contexts with which they are used. The main novelty of this project will be its multidisciplinary approach that combines expertise in digital privacy and security with expertise in the design disciplines of interaction, industrial, graphic, and architectural design. By involving diverse non-expert and expert participation in the design, development, and assessment of prototype solutions, this project will broaden participation in privacy and security research and education in order to generate solutions and strategies that are inclusive, appropriate, fair, and effective for everyone. A key aim of this project is to look for alternatives to the informed consent and usable choice approaches that currently drive most user-oriented privacy and security research. These approaches often fail: privacy policies are often ignored or impenetrable, and can actually decrease trust even when clearly written, while choice-based approaches often fail to fit privacy decisions and preferences that vary widely according to experience, personality, identity, ability, and situation. To address these issues, the research team will conduct qualitative fieldwork and participatory design research that invites diverse non-expert and expert participation. This multidisciplinary approach aims to contribute to privacy, security, and human-centered computing research by (1) generating novel multiform, socially-sensitive, and contextually-appropriate solutions to address current and future privacy and security issues, particularly those associated with the proliferation of connected, artificially-intelligent, and "always-on" everyday technologies, (2) broadening and diversifying participation in privacy and security problem framing, solutions-finding, and discourse, and (3) developing forward-looking methods, strategies, and exemplar prototypes for engineering, computer science, design, policymaking, and other experts and professionals to anticipate and respond to future privacy and security concerns.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.