DMUU: Statistical Disclosure Limitation for Geospatial Image Data

National Institute Of Statistical Sciences, Durham NC

Investigators

Abstract

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).