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: 1537324
• 负责人: Helen Nissenbaum
• 金额: $ 24.6万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537324&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，隐私应该在设计过程中考虑到，而不仅仅是事后，就像经常发生的那样。然而，这一理想未能获得广泛的实际牵引力，部分原因是缺乏成熟的方法，也因为隐私的概念复杂性，这阻碍了其可操作性。该项目同时解决了这两个挑战，寻求（i）展示隐私的强大操作化如何导致有意义的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（如电子邮件，杂货店购物，看电视、交通、移动的设备等等。该项目采用隐私作为上下文完整性的定义，将重点关注PI Deborah Estrin小数据实验室目前“在绘图板上”的选定小数据应用程序。有了这些设计案例，该项目上升到PbD的挑战之一，即在开发的早期考虑隐私，作为一个研究企业，其主要目标是发现更多和更少的生产方法，从而产生与隐私要求相关的系统特性。与此同时，该项目将提供宝贵的见解，如何操作上下文的完整性，它设想的隐私作为适当的个人信息流，建模适当的流动符合上下文特定的信息规范，其中规定，（和禁止）信息流动根据三个参数：参与者（主体、发送者、接收者）、信息类型和传输原则（对流的功能约束）。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来评估隐私属性，并根据应用和使用的上下文来评估这些流。该项目还通过展示如何将信息规范整合到设计实践中，扩展了过去关于信息规范正式表示的工作。 除了其实质性的贡献，这个项目体现了一个创新的合作模式-一个新颖的配对的计算机科学家，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），在平等的伙伴关系，打造技术，体现有意义的隐私。