scholarly journals Runners, repeaters, strangers and aliens: Operationalising efficient output disclosure control

2020 ◽  
Vol 36 (4) ◽  
pp. 1281-1293
Author(s):  
Kyle Alves ◽  
Felix Ritchie
Keyword(s):  
Operations Management ◽  
Practical Implementation ◽  
Sensitive Data ◽  
Disclosure Control ◽  
Management Literature ◽  
Statistical Disclosure ◽  
Statistical Agencies ◽  
Checking Procedure ◽  
Staffing Needs ◽  
Research Facilities

Statistical agencies and other government bodies increasingly use secure remote research facilities to provide access to sensitive data for research and analysis by internal staff and third parties. Such facilities depend on human intervention to ensure that the research outputs do not breach statistical disclosure control (SDC) rules. Output SDC can be principles-based, rules-based, or something in between. Principles-based is often seen as the gold standard statistically, as it improves both confidentiality protection and utility of outputs. However, some agencies are concerned that the operational requirements are too onerous for practical implementation, despite these statistical advantages. This paper argues that the choice of output checking procedure should be seen through an operational lens, rather than a statistical one. We take a popular conceptualisation of customer demand from the operations management literature and apply it to the problem of output checking. We demonstrate that principles-based output SDC addresses user and agency requirements more effectively than other approaches, and in a way which encourages user buy-in to the process. We also demonstrate how the principles-based approach aligns better with the statistical and staffing needs of the agency.

scholarly journals Statistical disclosure control using random rounding and quadratic programming

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Neeraj Tiwari
Keyword(s):  
Quadratic Programming ◽  
Sensitive Information ◽  
Statistical Disclosure Control ◽  
Tabular Data ◽  
Minimum Loss ◽  
The Public ◽  
Disclosure Control ◽  
Confidential Data ◽  
Statistical Disclosure ◽  
Statistical Agencies

The most common method of providing data to the public is through statistical tables. The problem of protecting confidentiality in statistical tables containing sensitive information has been of great concern during the recent years. Rounding methods are perturbation techniques widely used by statistical agencies for protecting the confidential data. Random rounding is one of these methods. In this paper, using the technique of random rounding and quadratic programming, we introduce a new methodology for protecting the confidential information of tabular data with minimum loss of information. The tables obtained through the proposed method consist of unbiasedly rounded values, are additive and have specified level of confidentiality protection. Some numerical examples are also discussed to demonstrate the superiority of the proposed procedure over the existing procedures.

A CRITIQUE OF THE SENSITIVITY RULES USUALLY EMPLOYED FOR STATISTICAL TABLE PROTECTION

2002 ◽  
Vol 10 (05) ◽  
pp. 545-556 ◽  
Author(s):  
JOSEP DOMINGO-FERRER ◽  
VICENÇ TORRA
Keyword(s):  
Risk Assessment ◽  
A Priori ◽  
Tabular Data ◽  
Disclosure Control ◽  
Disclosure Risk ◽  
Dominance Rule ◽  
Statistical Table ◽  
Statistical Disclosure ◽  
A Cell ◽  
Statistical Agencies

In statistical disclosure control of tabular data, sensitivity rules are commonly used to decide whether a table cell is sensitive and should therefore not be published. The most popular sensitivity rules are the dominance rule, the p%-rule and the pq-rule. The dominance rule has received critiques based on specific numerical examples and is being gradually abandoned by leading statistical agencies. In this paper, we construct general counterexamples which show that none of the above rules does adequately reflect disclosure risk if cell contributors or coalitions of them behave as intruders: in that case, releasing a cell declared non-sensitive can imply higher disclosure risk than releasing a cell declared sensitive. As possible solutions, we propose an alternative sensitivity rule based on the concentration of relative contributions. More generally, we suggest to complement a priori risk assessment based on sensitivity rules with a posteriori risk assessment which takes into account tables after they have been protected.

Using the Complex Measure in an Assessment of the Information Loss Due to the Microdata Disclosure Control

2019 ◽  
Vol 66 (1) ◽  
pp. 7-26 ◽  
Author(s):  
Andrzej Młodak
Keyword(s):  
Ordinal Data ◽  
Original Data ◽  
Information Loss ◽  
Original Method ◽  
Measurement Scales ◽  
Sensitive Data ◽  
Disclosure Control ◽  
Complex Measure ◽  
Statistical Disclosure ◽  
Information Optimization

The paper contains a proposal of original method of assessment of information loss resulted from an application of the Statistical Disclosure Control (SDC) conducted during preparation of the resulting data to the publication and disclosure to interested users. The SDC tools enable protection of sensitive data from their disclosure – both direct and indirect. The article focuses on pseudonimised microdata, i.e. individual data without fundamental identifiers, used for scientific purposes. This control is usually to suppress, swapping or disturbing of original data. However, such intervention is connected with the loss of some information. Optimization of choice of relevant SDC method requires then a minimization of such loss (and risk of disclosure of protected data). Traditionally used methods of measurement of such loss are not rarely sensitive to dissimilarities resulting from scale and scope of values of variables and cannot be used for ordinal data. Many of them weakly take also connections between variables into account, what can be important in various analyses. Hence, this paper is aimed at presentation of a proposal (having the source in papers by Zdzisław Hellwig) concerning use of a method of normalized and easy interpretable complex measure (called also the synthetic indicator) for connected features based on benchmark and anti–benchmark of development to the assessment of information loss resulted from an application of some SDC techniques and at studying its practical utility. The measure is here constructed on the basis of distances between original data and data after application of the SDC taking measurement scales into account.

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

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.

Sign in / Sign up

Export Citation Format

Share Document