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Working toward effective anonymization for surveillance data: innovation at South Africa’s Agincourt Health and Socio-Demographic Surveillance Site

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Linking people and places is essential for population-health-environment research. Yet, this data integration requires geographic coding such that information reflecting individuals or households can appropriately be connected with characteristics of their proximate environments. However, offering access to such geocoding greatly increases the risk of respondent identification and, therefore, holds the potential to breach confidentiality. In response, a variety of “geographic masking” techniques have been developed to introduce error into geographic coding and thereby reduce the likelihood of identification. We report findings from analyses of the error introduced by several masking techniques applied to data from the Agincourt Health and Socio-Demographic Surveillance System in rural South Africa. Using a vegetation index (Normalized Difference Vegetation Index (NDVI)) at the household scale, comparisons are made between the “true” NDVI values and those calculated after masking. We also examine the tradeoffs between accuracy and protecting respondent privacy. The exploration suggests that in this study setting and for NDVI, geomasking approaches that use buffers and account for population density produce the most accurate results. However, the exploration also clearly demonstrates the tradeoff between accuracy and privacy, with more accuracy resulting in a higher level of potential respondent identification. It is important to note that these analyses illustrate a process that should characterize spatially informed research but within which particular decisions must be shaped by the research setting and objectives. In the long run, we aim to provide insight into masking’s potential and perils to facilitate population-environment-health research.

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  1. The NDVI Values and Quality Assessment (QA) files were obtained from the Land Satellites Data System (LSDS) Science Research and Development (LSRD) repository provided by the US Geological Survey (USGS) Earth Resources Observation and Science (EROS) center (LSRD, 2018).

  2. Calculations were also made with 1 km buffers with no substantial differences in overarching conclusions.

  3. We also examined the impact of removing boundary constraints which increases the distance of displacement for households by an average of 14%. However, the displacement distances varied substantially across villages. For example, villages with lower household density did not see large gains in displacement as compared to when with masking methods that account for household density. Also, household displacement distances were on average, lower when using k-anonymity methods. In all, this suggests that the household density is more limiting in terms of constraining displacement distances than the village boundary.


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Early versions of this manuscript were presented at the 2019 Annual Meeting of the Population Association of America, as well as the 2019 Conference on Demographic Responses to Changes in the Natural Environment. The latter was supported in part by an R13 grant from the Eunice Kennedy Shriver National Institute on Child Health and Human Development (#HD096853). We thank those who provided useful feedback at each venue.


Funding for this research was provided by the University of Colorado Boulder’s Research and Innovation Office. This research has also benefited from research, administrative, and computing support provided by the University of Colorado Population Center (Project 2P2CHD066613-06), funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The content is solely the responsibility of the author and does not necessarily represent the official views of the CUPC, NIH or CU Boulder. Indirect support was provided by the Wellcome Trust (Agincourt Unit, grant 085477/Z/08/Z) through its support of the MRC/Wits Rural Public Health and Health Transitions Research Unit. Indirect support was also provided by the University of Colorado Boulder's Earth Lab supported by CIRES and the Grand Challenge Initiative at CU Boulder. The authors would like to thank the communities, respondents, field staff, and management of the Agincourt Unit for their respective contributions to the production of the data used in this study.

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Correspondence to Lori M. Hunter.

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Table 3 Estimated coefficients and significance of difference between true vs. displaced median NDVI estimates, nine illustrative geomasking approaches
Table 4 Estimated coefficients and significance of difference between true and displaced sum NDVI estimates divided by the number of households, nine geomasking approaches
Table 5 Estimated distance and actual k-anonymity between NDVI estimates provided by seven geomasking approaches relative to the NDVI estimate for true household locations

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Hunter, L.M., Talbot, C., Twine, W. et al. Working toward effective anonymization for surveillance data: innovation at South Africa’s Agincourt Health and Socio-Demographic Surveillance Site. Popul Environ 42, 445–476 (2021).

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  • Anonymity
  • Confidentiality
  • Geomasking
  • Geographic Masking
  • Jittering
  • Spatial Error
  • Agincourt
  • South Africa