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Statistical information recovery from multivariate noise-multiplied data, a computational approach

Journal Article


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 1 .

Authors


  •   Lin, Yan-Xia
  •   Mazur, Luke
  •   Sarathy, Rathin (external author)
  •   Muralidhar, Krishnamurty (external author)

Publication Date


  • 2018

Citation


  • Lin, Y., Mazur, L., Sarathy, R. & Muralidhar, K. (2018). Statistical information recovery from multivariate noise-multiplied data, a computational approach. Transactions on Data Privacy, 11 (1), 23-45.

Scopus Eid


  • 2-s2.0-85046151430

Number Of Pages


  • 22

Start Page


  • 23

End Page


  • 45

Volume


  • 11

Issue


  • 1

Place Of Publication


  • Spain

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 1 .

Authors


  •   Lin, Yan-Xia
  •   Mazur, Luke
  •   Sarathy, Rathin (external author)
  •   Muralidhar, Krishnamurty (external author)

Publication Date


  • 2018

Citation


  • Lin, Y., Mazur, L., Sarathy, R. & Muralidhar, K. (2018). Statistical information recovery from multivariate noise-multiplied data, a computational approach. Transactions on Data Privacy, 11 (1), 23-45.

Scopus Eid


  • 2-s2.0-85046151430

Number Of Pages


  • 22

Start Page


  • 23

End Page


  • 45

Volume


  • 11

Issue


  • 1

Place Of Publication


  • Spain