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

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.

2020 ◽  
Vol 3 (348) ◽  
pp. 7-24
Author(s):  
Michał Pietrzak

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.


2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Neeraj Tiwari

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.


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

Sign in / Sign up

Export Citation Format

Share Document