SCC-PG: Privacy and Fairness in Planning when using Third-Party, Heterogeneous Data Sources

SCC-PG：使用第三方异构数据源时规划的隐私和公平性

• 批准号: 1951852
• 负责人: Adam Aviv
• 金额: $ 14.92万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951852&HistoricalAwards=false
• 关键词: SCC; PG; Privacy; Fairness; Planning

Civic and community planning today is able to draw from a wealth of mobility and location data generated by residents who both actively and passively participate in data collection. Mobility information can be gleaned from a number of sources, which are often collected and maintained by third-parties for uses in multiple, heterogeneous applications. However, when combined, these third-party, heterogeneous data can paint a rich picture of movement through the urban landscape, that could be an important source of information for community planners. However, the naive inclusion of heterogeneous data sources, such as through machine learning, presents a number of technical and social challenges that impact the privacy of city residents and raise important questions around fairness and social justice. The data is not uniformly distributed across communities, potentially leaving out important populations and biasing algorithmic decision making, and the highly detailed location information may leak information about residents that should be protected. As part of this planning grant, this project will address basic research challenges associated with privacy as well as fairness in the application of heterogeneous data for community planning that take into account both resident participation and planner decision making. Including: (1) How do we both identify and mitigate potential biases in community planning that can arise from heterogeneous, third-party data sources? (2) How do we design privacy-aware algorithms that can merge disparate and incomplete data while providing meaningful results for community planning? (3) How do we combine principles of fairness and bias with privacy preserving data analysis methods? (4) How do we identify under-representation within data sources and explore factors that contribute to participation? (5) How do residents and community-planners understand privacy with respect to mobility data? Additionally, the project will seek to expand community partnership and outreach during the planning-grant process through community workshops to identify key aspects of the basic research and how the outcomes can be integrated into practice.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.

当今的公民和社区规划能够从积极和被动地参与数据收集的居民产生的大量流动性和位置数据中汲取灵感。可以从许多来源中收集移动性信息，这些来源通常是由第三方收集和维护的，用于在多个异质应用中使用。但是，当结合使用时，这些第三方，异质数据可以描绘出通过城市景观运动的丰富图片，这可能是社区规划者的重要信息来源。但是，幼稚地包含异质数据来源，例如通过机器学习，提出了许多技术和社会挑战，这些挑战会影响城市居民的隐私，并围绕公平和社会正义提出重要问题。数据并非统一分布在社区中，有可能列出重要人群和偏见算法的决策，并且高度详细的位置信息可能会泄露有关应保护的居民的信息。 作为这项计划赠款的一部分，该项目将解决与隐私相关的基础研究挑战，以及将异质数据应用于社区规划中的公平性，这些挑战都考虑到居民参与和计划者的决策。包括：（1）我们如何识别和减轻社区规划中可能由异质的第三方数据来源引起的潜在偏见？ （2）我们如何设计隐私感知算法，这些算法在为社区计划提供有意义的结果的同时，可以合并不同的数据和不完整的数据？ （3）我们如何将公平和偏见原则与保存数据分析方法相结合？ （4）我们如何确定数据源中的代表性不足并探索导致参与的因素？ （5）居民和社区规划者如何了解出行数据的隐私？此外，该项目将寻求通过社区研讨会在计划授予过程中扩展社区伙伴关系和推广，以确定基础研究的关键方面，以及如何将结果融入实践中。该奖项反映了NSF的法定任务，并被视为值得通过基金会的知识分子优点和更广泛的影响审查的评估来通过评估来获得支持。

项目成果

Users Can Deduce Sensitive Locations Protected by Privacy Zones on Fitness Tracking Apps

用户可以在健身追踪应用程序上推断出受隐私区保护的敏感位置

• DOI: 10.1145/3491102.3502136
• 发表时间: 2022
• 期刊: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22
• 作者: Mink, Jaron;Yuile, Amanda Rose;Pal, Uma;Aviv, Adam J;Bates, Adam
• 通讯作者: Bates, Adam