DMUU: Statistical Disclosure Limitation for Geospatial Image Data

DMUU：地理空间图像数据的统计披露限制

• 批准号: 0345441
• 负责人: Alan Karr
• 金额: $ 9.88万
• 依托单位: National Institute of Statistical Sciences
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-06-01 至 2006-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0345441&HistoricalAwards=false
• 关键词: DMUU; Statistical; Disclosure; Limitation; Geospatial

In order to be usable for scientific and policy purposes, remotely sensed data such as satellite images, which have become ubiquitous in the social and natural sciences, must be accorded the same confidentiality protection long provided to other forms of data. The project will produce initial formulations and techniques for statistical disclosure limitation (SDL) for geospatial image data, using longitudinal analyses to frame questions and as pointers to testbed data sets. In particular, the research will address the extent to which existing risk-utility formulations for SDL, existing techniques for record linkage and existing computational methods and systems, especially geographical information systems (GIS), can be adapted to yield immediate impact. In the process, the project team will develop new abstractions and techniques for record linkage, disclosure risk and data utility to multiple stakeholders. Scalable data structures and algorithms will be created that can handle complex, high-dimensional image data.Formulation, implementation and evaluation of sound decisions to deal with climate change depend significantly on longitudinal analyses of geospatial image data, at both the pre-decision (Is the problem getting worse?) and post-decision stages (Is the policy working?). Much such data is, however, collected without the consent or even the knowledge of data subjects such as landowners. In order that the scientific benefits of capturing image data outweigh privacy intrusions, confidentiality must be maintained. The research will provide techniques that enable effective use of longitudinal image while preserving privacy and accommodating profound differences between images and traditional---numerical or categorical---data. The project will yield a deeper understanding of privacy issues associated with geospatial image data, and serve as the basis for future research. This developmental award was supported as part of the Fiscal Year 2003 Human and Social Dynamics priority area special competition on Decision Making Under Uncertainty (DMUU).

遥感数据，例如卫星图像，在社会科学和自然科学领域已经无处不在，为了能够用于科学和政策目的，必须给予长期以来对其他形式的数据提供的同样的保密保护。该项目将编制地理空间图像数据统计披露限制（SDL）的初步公式和技术，使用纵向分析来提出问题并作为指向试验台数据集的指针。特别是，研究将探讨现有的SDL风险效用公式、现有的记录联系技术和现有的计算方法和系统，特别是地理信息系统（GIS），在多大程度上可以加以调整，以产生直接影响。在此过程中，项目团队将开发新的抽象和技术，用于记录链接、披露风险和对多个涉众的数据效用。将创建可扩展的数据结构和算法，以处理复杂的高维图像数据。应对气候变化的合理决策的制定、实施和评估在很大程度上取决于对地理空间图像数据的纵向分析，无论是在决策前（问题是否变得更糟？）还是决策后阶段（政策是否有效？）。然而，许多此类数据是在未经土地所有者等数据主体同意甚至不知情的情况下收集的。为了使捕获图像数据的科学利益超过隐私侵犯，必须保持机密性。该研究将提供能够有效利用纵向图像的技术，同时保护隐私，并适应图像与传统数字或分类数据之间的深刻差异。该项目将对与地理空间图像数据相关的隐私问题有更深入的了解，并为未来的研究奠定基础。该发展奖是2003财政年度人类和社会动态优先领域不确定决策特别竞赛的一部分。