| ds.listDisclosureSettings {dsBaseClient} | R Documentation |
Lists current values for disclosure control filters in all data repository servers.
ds.listDisclosureSettings(datasources = NULL)
datasources |
a list of |
This function lists out the current values of the eight disclosure filters in each of
the data repository servers specified by datasources argument.
The eight filters are explained below:
(1) nfilter.tab, the minimum non-zero cell count allowed in any cell if a contingency table is
to be returned. This applies to one dimensional and two dimensional tables of counts tabulated
across one or two factors and to tables of a mean of a quantitative variable tabulated across a
factor. Default usually set to 3 but a value of 1 (no limit) may be necessary, particularly if
low cell counts are highly probable such as when working with rare diseases. Five is also a
justifiable choice to replicate the most common threshold rule imposed by data releasers
worldwide, but it should be recognised that many census providers are moving to ten - but the
formal justification of this is little more than 'it is safer' and everybody is scared of
something going wrong - in practice it is very easy to get around any block and so it is
debatable whether the scientific cost outweighs the imposition of any threshold.
(2) nfilter.subset, the minimum non-zero count of observational units (typically individuals) in
a subset. Typically defaulted to 3.
(3) nfilter.glm, the maximum number of parameters in a regression model as a proportion of the
sample size in a study. If a study has 1000 observational units (typically individuals) being
used in a particular analysis then if nfilter.glm is set to 0.33 (its default value) the maximum
allowable number of parameters in a model fitted to those data will be 330. This disclosure
filter protects against fitting overly saturated models that can be disclosive. The choice of
0.33 is entirely arbitrary.
(4) nfilter.string, the maximum length of a string argument if that argument is to be subject to
testing of its length. Default value 80. The aim of this nfilter is to make it difficult for
hackers to find a way to embed malicious code in a valid string argument that is actively
interpreted.
(5) nfilter.string, Short to be used when a string must be specified but that when valid that
string should be short.
(6) nfilter.kNN applies to graphical plots based on working with the k nearest neighbours of
each point. nfilter.kNN specifies the minimum allowable value for the number of nearest
neighbours used, typically defaulted to 3.
(7) nfilter.levels specifies the maximum number of unique levels of a factor variable that can
be disclosed to the client. In the absence of this filter a user can convert a numeric variable
to a factor and see its unique levels which are all the distinct values of the numeric vector. To
prevent such disclosure we set this threshold to 0.33 which ensures that if a factor has unique
levels more than the 33
(8) nfilter.noise specifies the minimum level of noise added in some variables mainly used for
data visualizations. The default value is 0.25 which means that the noise added to a given
variable, follows a normal distribution with zero mean and variance equal to 25
variance of the given variable. Any value greater than this threshold can reduce the risk of
disclosure.
Server function called: listDisclosureSettingsDS
ds.listDisclosureSettings returns a list containing the current settings of the
nfilters in each study specified.
DataSHIELD Development Team
## Not run:
## Version 6, for version 5 see Wiki
# Connecting to the Opal servers
require('DSI')
require('DSOpal')
require('dsBaseClient')
builder <- DSI::newDSLoginBuilder()
builder$append(server = "study1",
url = "http://192.168.56.100:8080/",
user = "administrator", password = "datashield_test&",
table = "CNSIM.CNSIM1", driver = "OpalDriver")
builder$append(server = "study2",
url = "http://192.168.56.100:8080/",
user = "administrator", password = "datashield_test&",
table = "CNSIM.CNSIM2", driver = "OpalDriver")
builder$append(server = "study3",
url = "http://192.168.56.100:8080/",
user = "administrator", password = "datashield_test&",
table = "CNSIM.CNSIM3", driver = "OpalDriver")
logindata <- builder$build()
# Log onto the remote Opal training servers
connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")
# Call to list current disclosure settings in all data repository servers
ds.listDisclosureSettings(datasources = connections)
# Restrict call to list disclosure settings only to the first, or second DS connection (study)
ds.listDisclosureSettings(datasources = connections[1])
ds.listDisclosureSettings(datasources = connections[2])
# Clear the Datashield R sessions and logout
datashield.logout(connections)
## End(Not run)