SaTC: CORE: Small: Collaborative: Algorithms Everywhere: Identifying and Designing for Data Privacy Styles

分析我们在线和无数医疗，信用卡和其他数据库中的个人数据的计算算法可以使我们越来越容易从看似平凡的数据（例如某人在Facebook上“喜欢”的页面）中推断出关于我们的个人，私密细节（例如我们的个性，政治意识形态或性偏好）。人们可能没有注意到或不知道这些风险，如果他们注意到或不知道这些风险，他们必须不断决定他们可能会向哪些算法提供他们的个人信息，忽略哪些算法，以及谴责哪些算法是侵犯性的或不道德的。目前，围绕这些系统出现的行为在现有的技术设计实践中没有得到充分考虑。为了解决这个缺点，这项工作将回答以下问题：人们如何在数据收集和分析是其环境的连续特征的世界中导航？人们根据自己不同的隐私观念、态度和需求采取什么策略？如何设计互联网系统的界面和其他功能，以支持不同类型的数据隐私？该建议使用搜索相关的行为作为研究和设计的背景下，检查用户的适应性和响应普遍的数据收集。通过一系列的调查，项目团队将开发一种经过验证的隐私模式或风格的衡量标准，这些隐私模式或风格与人们习惯用于指导其数据隐私行为的技术、工具和信念有关。研究人员还将对数据保护工具的用户进行定性采访。这些访谈将确定这些用户采用哪些具体策略来保护他们的隐私免受算法分析的影响，以及产生关于这些策略的功效信念的心理模型。他们将创建一系列实验原型工具，以探索算法隐私的设计空间。总的来说，这项工作将扩大我们的技术设计词汇，能够解释不同风格的隐私实践。这项研究有可能通过描述人们在试图保护自己的隐私时所面临的具体挑战来显著改善隐私政策和技术。该奖项反映了NSF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。

The computational algorithms that analyze our personal data online and in myriad medical, credit card, and other databases can make it increasingly easy to infer personal, intimate details about us (such as our personality, political ideology, or sexual preference) from seemingly mundane data (such as which pages someone has "Liked" on Facebook). People may not notice or know about these risks, and if they do, they must make ongoing decisions about which algorithms they may be providing with their personal information, which to ignore, and which to decry as invasive or unethical. Currently, the behaviors that emerge around these systems are poorly accounted for in existing technology design practices. To address this shortcoming, this work will answer the questions: How do people navigate a world in which data collection and analysis is a continuous feature of their environment? What strategies do people adopt based on their varied privacy perceptions, attitudes, and needs? How can interfaces and other features of internet systems be designed to support different styles of data privacy?This proposal uses search-related behaviors as a research and design context to examine user adaptation and response to pervasive data collection. Through a series of surveys, the project team will develop a validated measure of privacy patterns or styles related to the technologies, tools, and beliefs that people habitually use to guide their data privacy behaviors. The researchers also will conduct qualitative interviews with users of data protection tools. These interviews will establish what specific strategies these users employ to protect their privacy from algorithmic analysis, if any, as well as the mental models that give rise to efficacy beliefs about those strategies. They will create a collection of experimental prototype tools to explore the design space around algorithmic privacy. Collectively, this work will expand our technology design vocabulary in terms of being able to account for different styles of privacy practices. This research has the potential to significantly improve privacy policies and technologies by describing the specific challenges that people face as they attempt to protect their privacy.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.