Statistical Disclosure Control for Data Privacy Using Sequence of Generalised Linear Models

2016 ◽  
pp. 77-93 ◽  
Author(s):  
Min Cherng Lee ◽  
Robin Mitra ◽  
Emmanuel Lazaridis ◽  
An Chow Lai ◽  
Yong Kheng Goh ◽  
...  
Keyword(s):  
Data Privacy ◽  
Linear Models ◽  
Statistical Disclosure Control ◽  
Generalised Linear Models ◽  
Disclosure Control ◽  
Statistical Disclosure

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.

scholarly journals Statistical Disclosure Control for Data Privacy Preservation

2013 ◽  
Vol 80 (10) ◽  
pp. 38-43 ◽  
Author(s):  
Sarat KumarChettri ◽  
Bonani Paul ◽  
Ajoy Krishna Dutta
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Statistical Disclosure Control ◽  
Disclosure Control ◽  
Statistical Disclosure

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.

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