EAGER: Collaborative: A Research Agenda to Explore Privacy in Small Data Applications

EAGER：协作：探索小数据应用程序隐私的研究议程

• 批准号: 1818586
• 负责人: Helen Nissenbaum
• 金额: $ 19.16万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818586&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Agenda; Explore

One of the crucial ideas behind Privacy by Design (PbD) is that privacy should be taken into consideration in the process of design, not merely after-the-fact, as so often happens. Yet, this ideal has failed to gain widespread practical traction, challenged, in part, by the lack of developed methodologies and also because of privacy's conceptual complexity, which hampers its operationalization. This project addresses both challenges simultaneously, seeking (i) to demonstrate how a robust operationalization of privacy can lead to meaningful PbD and (ii) to contribute methodological insight by engaging with ongoing research and development in the area of small data applications, namely, systems that advance wellness and personal productivity by utilizing digital traces from individuals' day-to-day activities, such as e-mail, grocery shopping, TV watching, transportation, mobile devices, and so forth. Adopting the definition of privacy as contextual integrity, the project will focus on selected small-data applications currently "on the drawing board" in PI Deborah Estrin's Small Data lab. With these design cases, the project rises to one of the PbD challenges, namely consideration of privacy early on in development and, as a research enterprise, its primary aim is to uncover more and less productive methodological approaches for doing so, resulting in system characteristics well correlated with privacy requirements. At the same time, the project will provide invaluable insight into how to operationalize contextual integrity, which conceives of privacy as appropriate flow of personal information, modeling appropriate flow as conformance with context-specific informational norms, which, prescribe (and prohibit) information flows according to three parameters: actors (subject, sender, recipient), information types, and transmission principles (functional constraints on flow). Adopting contextual integrity as an operational definition means that researchers will assess privacy properties by carefully mapping data flows, and evaluating these flows in terms of the context of application and use. The project also extends past work on formal representations of informational norms by demonstrating how they may be integrated into design practices. In addition to its substantive contributions this project embodies an innovative collaborative model -- a novel pairing of a computer scientist, Deborah Estrin (Cornell), with a philosopher, Helen Nissenbaum (NYU), in an equal partnership to forge technologies that embody meaningful privacy.

隐私设计 (PbD) 背后的关键思想之一是，应在设计过程中考虑隐私，而不仅仅是像经常发生的那样事后考虑。然而，这一理想未能获得广泛的实际吸引力，部分原因是缺乏成熟的方法论，也因为隐私概念的复杂性阻碍了其可操作性。该项目同时解决了这两个挑战，力求（i）展示隐私的稳健操作如何能够带来有意义的 PbD，以及（ii）通过参与小数据应用领域的持续研究和开发来提供方法论见解，即通过利用个人日常活动（例如电子邮件、杂货购物、电视观看、交通、移动设备等）的数字痕迹来提高健康和个人生产力的系统。 等等。该项目采用上下文完整性作为隐私的定义，将重点关注 PI Deborah Estrin 小数据实验室目前“处于绘图阶段”的选定小数据应用程序。通过这些设计案例，该项目面临 PbD 挑战之一，即在开发早期就考虑隐私，作为一家研究企业，其主要目标是发现更多或更少生产力的方法来实现这一目标，从而产生与隐私要求密切相关的系统特征。同时，该项目将为如何操作情境完整性提供宝贵的见解，其中将隐私视为个人信息的适当流动，将适当的流动建模为符合特定情境的信息规范，根据三个参数规定（和禁止）信息流动：参与者（主体、发送者、接收者）、信息类型和传输原则（流动的功能约束）。采用上下文完整性作为操作定义意味着研究人员将通过仔细映射数据流并根据应用程序和使用的上下文评估这些流来评估隐私属性。该项目还通过展示如何将信息规范的正式表示集成到设计实践中，扩展了过去关于信息规范的正式表示的工作。 除了实质性贡献之外，该项目还体现了一种创新的合作模式——计算机科学家黛博拉·埃斯特林（康奈尔大学）与哲学家海伦·尼森鲍姆（纽约大学）的新颖配对，以平等的伙伴关系打造体现有意义的隐私的技术。