ds.listDisclosureSettings {dsBaseClient}R Documentation



Lists current values for disclosure control filters in all Opal servers


ds.listDisclosureSettings(datasources = NULL)



a list of the particular Opal servers to have their values listed


This function lists out the current values of the eight disclosure filters in each of the Opal servers specified by datasources. Eight filters can currently be set: (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 round 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 0.37 (its default value) the maximum allowable number of parameters in a model fitted to those data will be 370. This disclosure filter protects against fitting overly saturated models which can be disclosive. The choice of 0.37 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.stringShort 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 absense 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 0.10 which means that the noise added to a given variable, follows a normal distribution with zero mean and variance equal to 10 variance of the given variable. Any value greated than this threshold can reduce the risk of disclosure.


a list containing the current settings of the nfilters in each study specified


Paul Burton, Demetris Avraam for DataSHIELD Development Team


## Not run: 
##Client-side function call to list current disclosure settings in all Opal servers
##Equivalent call directly to server-side function to list current disclosure settings in all
##Opal servers not recommended unless you are experienced DataSHIELD user
##Call to client-side function and save output as an R object to refer to later
##Interrogate ouput later
#Restrict call to list disclosure settings only to the first, or second Opals

## End(Not run)

[Package dsBaseClient version 5.0.0 ]