scholarly journals Statistical Disclosure Control Methods for Microdata from the Labour Force Survey

2020 ◽  
Vol 3 (348) ◽  
pp. 7-24
Author(s):  
Michał Pietrzak
Keyword(s):  
Labour Force ◽  
Control Methods ◽  
Labour Force Survey ◽  
Statistical Disclosure Control ◽  
Disclosure Control ◽  
Original Dataset ◽  
Level Data ◽  
Statistical Disclosure ◽  
The Impact

The aim of this article is to analyse the possibility of applying selected perturbative masking methods of Statistical Disclosure Control to microdata, i.e. unit‑level data from the Labour Force Survey. In the first step, the author assessed to what extent the confidentiality of information was protected in the original dataset. In the second step, after applying selected methods implemented in the sdcMicro package in the R programme, the impact of those methods on the disclosure risk, the loss of information and the quality of estimation of population quantities was assessed. The conclusion highlights some problematic aspects of the use of Statistical Disclosure Control methods which were observed during the conducted analysis.

scholarly journals Addressing Disclosure Concerns and Analysis Demands in a Real-Time Online Analytic System

2013 ◽  
Vol 29 (1) ◽  
pp. 99-124 ◽  
Author(s):  
Tom Krenzke ◽  
Jane F. Gentleman ◽  
Jianzhu Li ◽  
Chris Moriarity
Keyword(s):  
Real Time ◽  
Survey Data ◽  
State Level ◽  
Statistical Disclosure Control ◽  
Health Statistics ◽  
The Public ◽  
Disclosure Control ◽  
Level Data ◽  
Statistical Disclosure ◽  
Interview Survey

Abstract This article focuses on methods for enhancing access to survey data produced by government agencies. In particular, the National Center for Health Statistics (NCHS) is developing methods that could be used in an interactive, integrated, real-time online analytic system (OAS) to facilitate analysis by the public of both restricted and public use survey data. Data from NCHS’ National Health Interview Survey (NHIS) are being used to investigate, develop, and evaluate such methods. We assume the existence of public use microdata files, as is the case for the NHIS, so disclosure avoidance methods for such an OAS must account for that critical constraint. Of special interest is the analysis of state-level data because health care is largely administered at the state level in the U.S., and state identifiers are not on the NHIS public use files. This article describes our investigations of various possible choices of methods for statistical disclosure control and the challenges of providing such protection in a real-time OAS that uses restricted data. Full details about the specific disclosure control methods used by a working OAS could never be publicly released for confidentiality reasons. NCHS is still evaluating whether to implement an OAS that uses NHIS restricted data, and this article provides a snapshot of a research and developmental project in progress.

Information loss resulting from statistical disclosure control of output data

2020 ◽  
Vol 65 (9) ◽  
pp. 7-27
Author(s):  
Andrzej Młodak
Keyword(s):  
Inverse Correlation ◽  
Original Data ◽  
Information Loss ◽  
Sensitive Information ◽  
Statistical Disclosure Control ◽  
Output Data ◽  
Specific Data ◽  
Disclosure Control ◽  
Statistical Disclosure

The most important methods of assessing information loss caused by statistical disclosure control (SDC) are presented in the paper. The aim of SDC is to protect an individual against identification or obtaining any sensitive information relating to them by anyone unauthorised. The application of methods based either on the concealment of specific data or on their perturbation results in information loss, which affects the quality of output data, including the distributions of variables, the forms of relationships between them, or any estimations. The aim of this paper is to perform a critical analysis of the strengths and weaknesses of the particular types of methods of assessing information loss resulting from SDC. Moreover, some novel ideas on how to obtain effective and well-interpretable measures are proposed, including an innovative way of using a cyclometric function (arcus tangent) to determine the deviation of values from the original ones, as a result of SDC. Additionally, the inverse correlation matrix was applied in order to assess the influence of SDC on the strength of relationships between variables. The first presented method allows obtaining effective and well- -interpretable measures, while the other makes it possible to fully use the potential of the mutual relationships between variables (including the ones difficult to detect by means of classical statistical methods) for a better analysis of the consequences of SDC. Among other findings, the empirical verification of the utility of the suggested methods confirmed the superiority of the cyclometric function in measuring the distance between the curved deviations and the original data, and also heighlighted the need for a skilful correction of its flattening when large value arguments occur.

scholarly journals Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 191
Author(s):  
Bernhard Meindl ◽  
Matthias Templ
Keyword(s):  
Statistical Disclosure Control ◽  
Software Environment ◽  
Web Based ◽  
Disclosure Control ◽  
Data Anonymization ◽  
Know How ◽  
Whole Process ◽  
Statistical Disclosure ◽  
Anonymized Data ◽  
The Impact

The interactive, web-based point-and-click application presented in this article, allows anonymizing data without any knowledge in a programming language. Anonymization in data mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological issues as well as know-how from subject matter specialists should be taken into account when anonymizing data. Even though specialized software such as sdcMicro exists, it is often difficult for nonexperts in a particular software and without programming skills to actually anonymize datasets without an appropriate app. The presented app is not restricted to apply disclosure limitation techniques but rather facilitates the entire anonymization process. This interface allows uploading data to the system, modifying them and to create an object defining the disclosure scenario. Once such a statistical disclosure control (SDC) problem has been defined, users can apply anonymization techniques to this object and get instant feedback on the impact on risk and data utility after SDC methods have been applied. Additional features, such as an Undo Button, the possibility to export the anonymized dataset or the required code for reproducibility reasons, as well its interactive features, make it convenient both for experts and nonexperts in R—the free software environment for statistical computing and graphics—to protect a dataset using this app.

scholarly journals Rounding based continuous data discretization for statistical disclosure control

2019 ◽  
Author(s):  
Navoda Senavirathne ◽  
Vicenç Torra
Keyword(s):  
Machine Learning ◽  
Data Protection ◽  
Data Privacy ◽  
Alternative Methods ◽  
Continuous Data ◽  
Statistical Disclosure Control ◽  
Disclosure Control ◽  
Discretization Methods ◽  
Statistical Disclosure ◽  
The Impact

Abstract “Rounding” can be understood as a way to coarsen continuous data. That is, low level and infrequent values are replaced by high-level and more frequent representative values. This concept is explored as a method for data privacy with techniques like rounding, microaggregation, and generalisation. This concept is explored as a method for data privacy in statistical disclosure control literature with perturbative techniques like rounding, microaggregation and non-perturbative methods like generalisation. Even though “rounding” is well known as a numerical data protection method, it has not been studied in depth or evaluated empirically to the best of our knowledge. This work is motivated by three objectives, (1) to study the alternative methods of obtaining the rounding values to represent a given continuous variable, (2) to empirically evaluate rounding as a data protection technique based on information loss (IL) and disclosure risk (DR), and (3) to analyse the impact of data rounding on machine learning based models. Here, in order to obtain the rounding values we consider discretization methods introduced in the unsupervised machine learning literature along with microaggregation and re-sampling based approaches. The results indicate that microaggregation based techniques are preferred over unsupervised discretization methods due to their fair trade-off between IL and DR.

Density-based microaggregation for statistical disclosure control

2010 ◽  
Vol 37 (4) ◽  
pp. 3256-3263 ◽  
Author(s):  
Jun-Lin Lin ◽  
Tsung-Hsien Wen ◽  
Jui-Chien Hsieh ◽  
Pei-Chann Chang
Keyword(s):  
Statistical Disclosure Control ◽  
Disclosure Control ◽  
Statistical Disclosure
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