Statistical Disclosure Control for Micro-Data Using theRPackagesdcMicro

Abstract

The demand for data from surveys, censuses or registers containing sensible information on people or enterprises has increased significantly over the last years. However, before data can be provided to the public or to researchers, confidentiality has to be respected for any data set possibly containing sensible information about individual units. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data.The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class implementation of SDC methods to evaluate and anonymize confidential micro-data sets. It includes all popular disclosure risk and perturbation methods. The package performs automated recalculation of frequency counts, individual and global risk measures, information loss and data utility statistics after each anonymization step. All methods are highly optimized in terms of computational costs to be able to work with large data sets. Reporting facilities that summarize the anonymization process can also be easily used by practitioners. We describe the package and demonstrate its functionality with a complex household survey test data set that has been distributed by the International Household Survey Network.

Prevalence of Endocrine and Metabolic Comorbidities in a National Cohort of Patients with Craniopharyngioma

“…Statistical analysis was conducted using the Statistical Package for Social Sciences (SPSS 25) as well as with R programming language [28]. The evaluations in each case were based on the number of patients with available data.…”

Improving efficiency of providing data group anonymity by automating data modification quality evaluation

“…-compatibilities of these solutions with imposed restrictions satisfy (2) because μ(q* 1 )=Z MF (13,10,97)•Z MF (8,10, 45)=0.990>α comp ; μ(q* 2 )=Z MF (11, 10, Based on these observations, we can conclude that the solutions presented in Fig. 2 are feasible.…”

“…In [9], enhanced methods of data generalization and suppression are proposed, which enable us to reduce the corresponding bias whilst providing k-anonymity. Methods of providing k-anonymity are implemented in the sdcMicro package for the R system [10].…”

A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis

“…In particular, we evaluate its performance in terms of data utility and disclosure risk in two scenarios: a) when it is applied to get a masked data set that protects all the variables in the data set, and b) when only a subset of variables needs to be protected and the output is a hybrid data set. The results have been obtained using R project (R Core Team 2014) and, more specifically, package sdcMicro (Templ 2008) when possible. Some ad hoc functions and programs also needed to be developed.…”