CAREER: Usable, Data-Driven Transparency and Access for Consumer Privacy

职业：可用、数据驱动的透明度和消费者隐私访问

• 批准号: 2047827
• 负责人: Blase Ur
• 金额: $ 54.95万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047827&HistoricalAwards=false
• 关键词: CAREER; Usable; Data; Driven; Transparency

Data collected about consumers underpins online personalization, yet raises critical privacy concerns. Mitigating these concerns requires giving consumers awareness and access to, data collected about them. To date, though, consumers have been given limited insight through these mechanisms. Attempts to provide awareness have centered on static disclosures of limited utility. Access rights for collected data have mostly been ignored or manifested as meaningless database exports. Unfortunately, consumers struggle to learn what has been collected, by whom, and especially what this collection implies for their privacy. This project aims to improve data transparency and access rights by developing novel models, user interactions, and open-source privacy tools. These techniques and tools will empower consumers to protect their privacy in a data-driven world. Furthermore, the project will enhance education and outreach. The investigator will develop a pilot program for engaging early-career students in research, create a new privacy course for underprivileged high school students, and both develop and exhibit artworks designed to provoke reflection on individual privacy, by collaborating with the School of the Art Institute of Chicago and the Chicago Museum of Science and Industry.The project will center around the intellectual development of complementary tools that give consumers data-driven insight into (i) the huge archives companies give consumers who exercise data-access rights and (ii) the online third-party tracking ecosystem. Several intellectual challenges stand in the way. First, usable transparency about voluminous data requires automatic methods that surface the information that matters most to consumers. The project will address this challenge through formative user studies and the iterative development and evaluation of a data-access tool. In addition, the project will develop new techniques for identifying, integrating, and visualizing data archives. Second, because the raw data collected about a consumer provides only partial transparency, the project will develop new methods for communicating, in an individualized manner, inferences possible from data. Third, as transparency is powerless without recourse, the project will understand the types of recourse users desire and develop tools that identify and mitigate privacy-tool-related website breakage at scale.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.

收集到的消费者数据为在线个性化提供了支撑，但也引发了关键的隐私问题。减轻这些担忧需要让消费者意识到并获得收集到的有关他们的数据。然而，到目前为止，消费者对这些机制的了解有限。提供认识的尝试集中在有限效用的静态披露上。收集数据的访问权限大多被忽略或表现为无意义的数据库导出。不幸的是，消费者很难知道被谁收集了什么，尤其是这些收集对他们的隐私意味着什么。该项目旨在通过开发新模型、用户交互和开源隐私工具来提高数据透明度和访问权限。这些技术和工具将使消费者能够在数据驱动的世界中保护自己的隐私。此外，该项目将加强教育和外联。通过与芝加哥艺术学院和芝加哥科学与工业博物馆合作，研究者将开发一个试点项目，吸引早期职业学生参与研究，为贫困的高中生创建一个新的隐私课程，并开发和展示旨在引发对个人隐私反思的艺术品。该项目将围绕辅助工具的智能开发，为消费者提供数据驱动的洞察(i)公司为行使数据访问权的消费者提供的庞大档案，以及（ii）在线第三方跟踪生态系统。几个智力上的挑战挡在了路上。首先，要实现海量数据的可用透明度，就需要采用自动方法将对消费者最重要的信息显示出来。该项目将通过形成性用户研究和数据访问工具的迭代开发和评价来应对这一挑战。此外，该项目将开发用于识别、集成和可视化数据档案的新技术。其次，由于收集到的关于消费者的原始数据只提供了部分透明度，因此该项目将开发新的方法，以个性化的方式，从数据中推断出可能的结果。第三，如果没有追索权，透明度是无能为力的，该项目将了解用户所希望的追索权类型，并开发工具来大规模识别和减轻与隐私工具相关的网站破坏。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Analysis of Google Ads Settings Over Time: Updated, Individualized, Accurate, and Filtered

随着时间的推移分析 Google 广告设置：更新、个性化、准确和过滤

• DOI: 10.1145/3603216.3624968
• 发表时间: 2023
• 期刊: Proceedings of the 21st Workshop on Privacy in the Electronic Society
• 作者: Reitinger, Nathan;Wen, Bruce;Mazurek, Michelle L.;Ur, Blase
• 通讯作者: Ur, Blase

JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data Scientists

JupyterLab 逆行：强调数据科学家公平性和偏见问题的上下文通知

• 发表时间: 2024
• 期刊: Proceedings of the CHI Conference on Human Factors in Computing Systems
• 作者: Harrison, Galen;Bryson, Kevin;Bamba, Ahmad Emmanuel;Dovichi, Luca;Binion, Aleksander Herrmann;Borem, Arthur;Ur, Blase
• 通讯作者: Ur, Blase

Defining "Broken": User Experiences and Remediation Tactics When Ad-Blocking or Tracking-Protection Tools Break a Website’s User Experience

定义“损坏”：广告拦截或跟踪保护工具破坏网站用户体验时的用户体验和补救策略

• 发表时间: 2023
• 期刊: Proceedings of the 32nd USENIX Security Symposium
• 作者: Nisenoff, Alexandra;Borem, Arthur;Pickering, Madison;Nakanishi, Grant;Thumpasery, Maya;Ur, Blase
• 通讯作者: Ur, Blase

Data Subjects’ Reactions to Exercising Their Right of Access

数据主体对行使其访问权的反应

• 发表时间: 2024
• 期刊: Proceedings of the 33rd USENIX Security Symposium
• 作者: Borem, Arthur;Pan, Elleen;Obielodan, Olufunmilola;Roubinowitz, Aurelie;Dovichi, Luca;Mazurek, Michelle L.;Ur, Blase
• 通讯作者: Ur, Blase

Pursuing Usable and Useful Data Downloads Under GDPR/CCPA Access Rights via Co-Design

通过协同设计根据 GDPR/CCPA 访问权追求可用且有用的数据下载

• 发表时间: 2021
• 期刊: Proceedings of the USENIX Symposium on Usable Privacy and Security (SOUPS
• 作者: Veys, Sophie;Serrano, Daniel;Stamos, Madison;Herman, Margot;Reitinger, Nathan;Mazurek, Michelle L;Ur, Blase
• 通讯作者: Ur, Blase