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Volume 11 Issue 1

Statistical Information Recovery from Multivariate Noise-Multiplied Data, a Computational Approach

Yan-Xia Lin^(a),(*), Luke Mazur^(a), Rathin Sarathy^(b), Krishnamurty Muralidhar^(c)

Transactions on Data Privacy 11:1 (2018) 23 - 45

Abstract, PDF

(a) National Institution for Applied Statistics Research Australia, School of Mathematics and Applied Statistics, University of Wollongong, Australia.

(b) Spears School of Business, Oklahoma State University, Stillwater OK 74078, USA.

(c) Division of Marketing and Supply Chain Management, Price College of Business, The University of Oklahoma, USA.

e-mail:yanxia @uow.edu.au; lm810 @uowmail.edu.au; rathin.sarathy @okstate.edu; krishm @ou.edu

Abstract

This paper proposes a computational statistical method for multivariate confidential numerical microdata. The method can be employed for recovering some commonly interesting statistical information present in the microdata from noise-multiplied data. Estimating the parameters in linear regression without using the original data directly becomes feasible. This paper demonstrates that some statistical information can be recovered reasonably well for certain types of original data while the level of disclosure risk is under control if the multiplicative noises used to mask the data are appropriate.

This paper presents an alternative approach for sharing the statistical information of multivariate confidential data and carrying out data mining with multidimensional sensitive data, an area of growing interest.

An R package MaskJointDensity is built for implementing the method.

^* Corresponding author.

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