Creating a robust and reliable resource for accessing, sharing, and analyzing confidential geospatial research data: Overcoming obstacles to replicating NSF research
Prof. KWAN Mei-Po
Collaborator(s):
Douglas B. Richardson (PI) and Margaret Levenstein
Project period:
09/01/2018 - 02/28/2022
Funding source:
U.S. National Science Foundation
Funded amount:
US$600,000 (HK$4,680,000)
The ability to replicate and reproduce research is a corner-stone of the scientific method. The generation and analysis of geospatial and locational data is now at the frontier of many scientific domains. Yet, the unique characteristics of these data present special challenges to data sharing practices due to the need to protect the locational privacy and confidentiality of research subjects. This project seeks to develop a Geospatial Virtual Data Enclave (GVDE) prototype to support confidential geospatial research data sharing, protection, analysis, and use by NSF researchers.
It focuses on four components required to create and implement a robust and reliable GVDE system to provide a secure yet accessible environment to enable analysis and replication of NSF research and support the development of NSF data management plans: (a) Develop and enhance the GVDE and its core capabilities; (b) Evaluate and implement geomasking and encryption methods; (c) Develop and implement an innovative researcher credentialing system; and (d) Ensure the long-term sustainability of the GVDE system.
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Derivation of spatial k-anonymity (Source: Wang, Jue, and Mei-Po Kwan. 2020. Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets. International Journal of Health Geographics 19(7).) |
