DMUU: Statistical Disclosure Limitation for Geospatial Image Data

为了可用于科学和政策目的，必须对在社会科学和自然科学中已变得无处不在的卫星图像等遥感数据给予长期以来给予其他形式数据的同样保密保护。 该项目将提出地理空间图像数据统计披露限制的初步公式和技术，利用纵向分析来确定问题的框架，并作为试验台数据集的指针。 特别是，这项研究将探讨可持续土地发展现有的风险效用公式、现有的记录联系技术以及现有的计算方法和系统，特别是地理信息系统，在多大程度上可以加以调整，以产生直接的影响。 在这一过程中，项目小组将为记录链接、披露风险和向多个利益攸关方提供数据效用开发新的抽象概念和技术。 可扩展的数据结构和算法将被创建，可以处理复杂的，高维图像data.Formulation，实施和评估的健全的决策，以应对气候变化在很大程度上取决于纵向分析的地理空间图像数据，在两个决策前（问题变得更糟？）和决策后阶段（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。 为了使获取图像数据的科学利益超过隐私侵犯，必须保持机密性。 这项研究将提供技术，使有效地利用纵向图像，同时保护隐私和适应图像和传统的-数值或分类-数据之间的深刻差异。 该项目将使人们更深入地了解与地理空间图像数据相关的隐私问题，并作为未来研究的基础。 这一发展奖是作为2003财政年度人类和社会动态优先领域不确定性下决策特别竞赛的一部分得到支持的。

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).