Street masking: a network-based geographic mask for easily protecting geoprivacy.

Background: Geographic masks are techniques used to protect individual privacy in published maps but are highly under-utilized in research. This leads to continual violations of individual privacy, as sensitive health records are put at risk in unmasked maps. New approaches to geographic masking are required that foster accessibility and ease of use, such that they become more widely adopted.

Ensuring Confidentiality of Geocoded Health Data: Assessing Geographic Masking Strategies for Individual-Level Data.

Public health datasets increasingly use geographic identifiers such as an individual's address. Geocoding these addresses often provides new insights since it becomes possible to examine spatial patterns and associations. Address information is typically considered confidential and is therefore not released or shared with others.

Error propagation models to examine the effects of geocoding quality on spatial analysis of individual-level datasets.

The quality of geocoding has received substantial attention in recent years. A synthesis of published studies shows that the positional errors of street geocoding are somewhat unique relative to those of other types of spatial data: (1) the magnitude of error varies strongly across urban-rural gradients; (2) the direction of error is not uniform, but strongly associated with the properties of local street segments; (3) the distribution of errors does not follow a normal distribution, but is highly skewed and characterized by a substantial number of very large error values; and (4) the magnitude of error is spatially autocorrelated and is related to properties of the reference data. This makes it difficult to employ analytic approaches or Monte Carlo simulations for error propagation modeling because these rely on generalized statistical characteristics.