NDLI: Applicability of Regression-Tree-Based Synthetic Data Methods for Business Data

Content Provider	IEEE Xplore Digital Library
Author	Joo-Ho Lee In-Yong Kim O'Keefe, C.M.
Copyright Year	2011
Abstract	This paper concerns the use of synthetic data for protecting the confidentiality of business data during statistical analysis. Synthetic datasets are constructed by replacing sensitive values in a confidential dataset with draws from statistical models estimated on the confidential dataset. Unfortunately, the process of generating effective statistical models can be a difficult and labour-intensive task. Recently, it has been proposed to use easily-implemented methods from machine learning instead of statistical model estimation in the data synthesis task. J. Drechsler and J.P. Reiter [1] have conducted an evaluation of four such methods, and have found that regression trees could give rise to synthetic datasets which provide reliable analysis results as well as low disclosure risks. Their conclusion was based on simulations using a subset of the 2002 Uganda census public use file, and it is an interesting question whether the same conclusion applies to other types of data with different characteristics. For example, business data have quite different characteristics from population census and survey data. Business data generally have few variables that are mostly categorical, and often have highly skewed distributions with outliers. In this paper we investigate the applicability of regression-tree-based methods for constructing synthetic business data. We give a detailed example comparing exploratory data analysis and linear regression results under two variants of a regression-tree-based synthetic data approach. We also include an evaluation of the analysis results with respect to the results of analysis of the original data. We further investigate the impact of different stopping criteria on performance. Our example provides evidence that synthesisers based on regression trees may not be immediately applicable in the context of business data. Further investigation, including further simulation studies with larger datasets, is certainly indicated.
Starting Page	651
Ending Page	658
File Size	317710
Page Count	8
File Format	PDF
ISBN	9781467300056
DOI	10.1109/ICDMW.2011.32
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Data analysis Confidentiality Business Data Sugar industry Data models Imputation Disclosure Sugar Regression tree analysis Business
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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