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

设计隐私(Privacy by Design，PBD)背后的一个重要理念是，在设计过程中应该考虑隐私，而不是像经常发生的那样，只是在事后才考虑隐私。然而，这一理想未能获得广泛的实践吸引力，挑战的部分原因是缺乏已开发的方法，还因为隐私的概念复杂性阻碍了其可操作性。该项目同时应对这两个挑战，寻求(I)展示强大的隐私操作如何产生有意义的PBD，以及(Ii)通过参与小数据应用领域的持续研究和开发，提供方法学上的见解，即通过利用个人日常活动的数字痕迹来提高健康和个人生产力的系统，如电子邮件、食品杂货购物、看电视、交通、移动设备等。该项目采用隐私的定义为上下文完整性，将重点放在皮德博拉·埃斯特林的小数据实验室中目前“正在设计”的选定的小数据应用程序上。有了这些设计案例，该项目上升到PBD挑战之一，即在开发早期考虑隐私，作为一家研究企业，其主要目标是发现更多和更少生产力的方法方法，导致系统特征与隐私需求很好地关联。同时，该项目将为如何实施背景完整性提供宝贵的见解，将隐私视为适当的个人信息流，将适当的流建模为符合特定背景的信息规范，这些规范根据三个参数规定(和禁止)信息流：行为者(主体、发送者、接收者)、信息类型和传输原则(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并根据应用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纳入设计实践，扩展了过去关于信息规范的正式表述的工作。除了它的实质性贡献外，这个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(康奈尔大学饰)与哲学家海伦·尼森鲍姆(纽约大学饰)建立了平等的合作伙伴关系，以打造体现有意义隐私的技术。

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.