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. 2021 Jun;42(4):445-476.
doi: 10.1007/s11111-020-00372-4. Epub 2021 Mar 24.

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

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

Lori M Hunter et al. Popul Environ. 2021 Jun.
No abstract available

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Figures

Figure 1.
Figure 1.
A Conceptual Illustration of Geographic Masking (Zanderbergen 2014)
Figure 2.
Figure 2.
Example of a Simple Random Offset for 2 Households
Figure 3:
Figure 3:. Common Geographic Masking Techniques *
* black circle represents the original location; gray dots represent 700 simulated possible locations using each masking method within a radius of 300m with exclusion zones of 150 m for scenarios 3 and 4.
Figure 4.
Figure 4.
Villages within the MRC/Wits Agincourt Health Demographic Surveillance Site, South Africa
Figure 5.
Figure 5.. Estimated NDVI Values for a Randomly Selected Household, MRC/Wits Agincourt Unit
*Diamonds represent NDVI values during growing season months (September to April)
Figure 6.
Figure 6.
Random Geomasking within a Buffer Constrained by Village Boundaries
Figure 7.
Figure 7.
Three Geomasking Techniques with Randomization within Boundaries, MRC/Wits Agincourt-Unit
Figure 8.
Figure 8.
NDVI Values (Jan 2010) for True Household Locations, 2 km buffers, MRC/Wits Agincourt-Unit
Figure 9.
Figure 9.. True and Geomasked NDVI Values for a Randomly Selected Household, MRC/Wits Agincourt Unit*
*Blue = NDVI for true households; Green = NDVI for geomasked household (displaced randomly within village).
Figure 10.
Figure 10.
Spatial Distribution of Difference in Sum of NDVI Values/ Number of Households, True vs. Geomasked Locations, MRC/Wits Agincourt-Unit

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