SaTC: CORE: Medium: Collaborative: Enabling Long-Term Security and Privacy through Retrospective Data Management

SaTC：核心：媒介：协作：通过回顾性数据管理实现长期安全和隐私

• 批准号: 1801663
• 负责人: Blase Ur
• 金额: $ 41.6万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1801663&HistoricalAwards=false
• 关键词: SaTC; CORE; Medium; Collaborative; Enabling

Online data storage, everything from past conversations to tax returns to playdate invitations, may be retained at full fidelity for years or decades. Although the data being saved in online archives does not change, the personal and social contexts surrounding them do. Those life changes may necessitate changing or deleting stored data but, unfortunately, the vast quantity of data in users' online archives makes manual management infeasible. The goal of this project is to develop methods and tools that enable users to manage the data they have accumulated over many years, leveraging user-centered design and machine learning to partially automate the process. These tools will enable a better understanding of retrospective privacy in the context of modern long-lived online archives. They will also empower users to more effectively manage the risks embedded in these archives. The findings shared with the research community will advance discovery beyond this project. The understanding of user conceptualizations of security and privacy over time, as contexts change, has been stymied by a lack of broad, carefully collected datasets within this domain. This project will collect anonymized datasets, with users' permission, that enable further research in this area. The team is conducting one of the first longitudinal studies of how desired security and privacy decisions change over time. The project will also gather qualitative insights about users' perceptions of risk and utility for long-term data, as well as the acceptability of retrospective management mechanisms. Furthermore, it is not currently understood how temporality impacts the use of machine learning techniques for privacy, nor how to capture concept drift to ensure that latent threats can be identified within immense archives. These tasks require new machine learning approaches and predictive models that can both account for the temporal dimension and minimize user burden when automating archive management. Finally, the project will design and implement novel user-centered interfaces that address the currently unmet need of helping users efficiently minimize security and privacy risks in their large, long-term online archives.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.

在线数据存储，从过去的对话到纳税申报表，再到Playdate邀请函，可以保留数年或数十年。尽管保存在在线档案中的数据并没有改变，但周围的个人和社会环境确实会发生。这些生活变化可能需要更改或删除存储的数据，但不幸的是，用户在线档案中的大量数据使手动管理变得不可行。该项目的目的是开发方法和工具，使用户能够管理他们多年来积累的数据，利用以用户为中心的设计和机器学习来部分自动化该过程。这些工具将使在现代长期在线档案的背景下更好地理解回顾性隐私。他们还将授权用户更有效地管理这些档案中嵌入的风险。与研究社区共享的发现将使发现超越该项目。随着上下文的变化，对用户对安全性和隐私的概念的理解受到了该域内缺乏广泛的，精心收集的数据集的困扰。该项目将在用户许可的情况下收集匿名数据集，从而在该领域进行进一步的研究。该团队正在对期望的安全和隐私决策随时间变化的最早纵向研究之一。该项目还将收集有关用户对长期数据风险和实用性的看法以及回顾性管理机制的可接受性的定性见解。此外，目前尚不理解时间性如何影响机器学习技术用于隐私，也不了解如何捕获概念漂移以确保可以在巨大的档案中确定潜在威胁。这些任务需要新的机器学习方法和预测模型，这些模型既可以说明时间维度，又可以在自动化存档管理时减少用户负担。最后，该项目将设计和实施以用户为中心的新型界面，这些界面解决了当前未满足的需求，以帮助用户在其大型长期在线档案中有效地最大程度地降低安全性和隐私风险。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的，审查了审查的审查标准。

项目成果

Helping Users Automatically Find and Manage Sensitive, Expendable Files in Cloud Storage

• 发表时间: 2021
• 作者: Mohammad Taha Khan;Christopher Tran;Shubham Singh;Dimitri Vasilkov;Chris Kanich;Blase Ur;E. Zheleva

Poster: Making Retrospective Data Management Usable

海报：使回顾性数据管理可用

• 发表时间: 2018
• 期刊: Proceedings of the Fourteenth Symposium on Usable Privacy and Security (SOUPS
• 作者: Hirsch, Noah;Kanich, Chris;Khan, Mohammad Taha;Liu, Xuefeng;Mondal, Mainack;Tang, Michael;Tran, Christopher;Ur, Blase;Wang, William;Yilmaz, Günce Su
• 通讯作者: Yilmaz, Günce Su

Perceptions of Retrospective Edits, Changes, and Deletion on Social Media

• DOI: 10.1609/icwsm.v15i1.18108
• 发表时间: 2021-05
• 作者: Günce Su Yilmaz;Fiona Gasaway;Blase Ur;Mainack Mondal

Summarizing Sets of Related ML-Driven Recommendations for Improving File Management in Cloud Storage

• DOI: 10.1145/3526113.3545704
• 发表时间: 2022-10
• 期刊: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
• 作者: Will Brackenbury;K. Chard;Aaron J. Elmore;Blase Ur

Files of a Feather Flock Together? Measuring and Modeling How Users Perceive File Similarity in Cloud Storage

• DOI: 10.1145/3404835.3462845
• 发表时间: 2021-07
• 期刊: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Will Brackenbury;Galen Harrison;K. Chard;Aaron J. Elmore;Blase Ur