Protecting Values Close to Zero Under the Multiplicative Noise Method

  • Yan-Xia LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11126)


Perturbing sensitive data is one of the standard ways of protecting confidential data. The multiplicative noise method is one of these data perturbation methods and this method has attracted researchers’ attention in the recent decade. However, values close to zero in datasets cannot be well protected by using the multiplicative noise method directly. This paper proposes a method for safeguarding the values close to zero through noise-multiplied shifted data. We demonstrate that those values can be reasonably protected through noise-multiplied data by following the approach proposed in this paper. This paper also indicates that the density function of the original data can be reasonably reconstructed from the noise-multiplied shifted data by using the software MaskDensity14 or MaskDensityBM.


Confidential data Masked data Multiplicative noise method 


  1. 1.
    Hwang, J.T.: Multiplicative errors-in-variables models with applications to recent data released by the U.S. department of energy. J. Am. Stat. Assoc. 81, 680–688 (1986)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Kim, J., Winkler, W.: Multiplicative noise for masking continuous data. Technical report, Statistical Research Division, U.S. Bureau of the Census, Washinton D.C. 20233 (2003)Google Scholar
  3. 3.
    Kim, J., Jeong, D.: Truncated triangular distribution for multiplicative noise and domain estimation. In: Government Statistics-JSM, pp. 1023–1030 (2008)Google Scholar
  4. 4.
    Oganian, A.: Multiplicative noise for masking numerical microdata with constraints. “SORT”, Special Issue, pp. 99–112 (2011). ISSN 1696-2281
  5. 5.
    Ruiz, N.: A multiplicative masking method for preserving the skewness of the original micro-records. J. Off. Stat. 28, 107–120 (2012)Google Scholar
  6. 6.
    Lin, Y.X., Wise, P.: Estimation of regression parameters from noise multiplied data. J. Priv. Confid. 4, 55–88 (2012)Google Scholar
  7. 7.
    Klein, M., Mathew, T., Sinha, B.: Noise multiplication for statistical disclosure control of extreme values in log-normal regressopm samples. J. Priv. Confid. 6, 77–125 (2014)Google Scholar
  8. 8.
    Lin, Y.-X.: Density approximant based on noise multiplied data. In: Domingo-Ferrer, J. (ed.) PSD 2014. LNCS, vol. 8744, pp. 89–104. Springer, Cham (2014). Scholar
  9. 9.
    Mivule, K.: Utilizing noise addition for data privacy, an overview. In: Proceedings of the International Conference on Information and Knowledge Engineering (IKE2012), The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), pp. 65–71 (2012)Google Scholar
  10. 10.
    Mendes, R., Vilela, J.P.: Privacy-preserving data mining: methods, metrics, and applications. IEEE Access 5, 1–21 (2017)CrossRefGoogle Scholar
  11. 11.
    Torra, V.: Data Privacy: Foundations, New Developments and the Big Data Challenge. SBD, vol. 28. Springer, Cham (2017). Scholar
  12. 12.
    Nayak, T.K., Sinha, B., Zayatz, L.: Statistical properties of multiplicative noise masking for confidentiality protection. J. Off. Stat. 27(3), 527–544 (2011)Google Scholar
  13. 13.
    Muralidhar, K., Batra, D., Kirs, P.J.: Accessibility, security, and accuracy in statistical databases: the case for the multiplicative fixed data perturbation approach. Manag. Sci. 41(9), 1549–1564 (1995)CrossRefGoogle Scholar
  14. 14.
    Lin, Y.X., Mazur, L., Sarathy, R., Muralidhar, K.: Statistical information recovery from multivariate noise-multiplied data, a computational approach. Trans. Data Priv. 11, 23–45 (2018)Google Scholar
  15. 15.
    Sinha, B., Nayak, T., Zayatz, L.: Privacy protection and quantile estimation from noise multiplied data. Sankhya B 73, 297–315 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Oliveira, S.R.M., Zaiane, O.R.: Privacy preserving clustering by data transformation. J. Inf. Data Manag. 1, 37–51 (2010)Google Scholar
  17. 17.
    Adam, N.R., Worthmann, J.C.: Security-control methods for statistical databases: a comparative study. ACM Comput. Surv. 21, 515–556 (1989)CrossRefGoogle Scholar
  18. 18.
    Muralidhar, K., Parsa, R., Sarathy, R.: A general additive data perturbation method for database security. Manag. Sci. 45(10), 1399–1415 (1999)CrossRefGoogle Scholar
  19. 19.
    Ma, Y., Lin, Y.X., Sarathy, R.: The vulnerability of multiplicative noise protection to correlational attacks on continuous microdata. Technical report, National Institute for Applied Statistics Research Australia, School of Mathematics and Applied Statistics, University of Wollongong, Australia (2017)Google Scholar
  20. 20.
    Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 247–255. ACM (2001)Google Scholar
  21. 21.
    Burridge, J.: Information preserving statistical obfuscation. Stat. Comput. 13, 321–327 (2003)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Domingo-Ferrer, J., Sebé, F., Castellà-Roca, J.: On the security of noise addition for privacy in statistical databases. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 149–161. Springer, Heidelberg (2004). Scholar
  23. 23.
    Lin, Y.X., Fielding, M.J.: Maskdensity14: An R package for the density approximant of a univariate based on noise multiplied data. SoftwareX 3, 37–43 (2015)CrossRefGoogle Scholar
  24. 24.
    Lin, Y.X.: Mining the statistical information of confidential data from noise-multiplied data. In: Proceedings of the 3rd IEEE International Conference on Big Data Intelligence and Computing (2017)Google Scholar
  25. 25.
    Lin, Y.X.: A computational Bayesian approach for estimating density functions based on noise-multiplied data. Int. J. Big Data Intell. (2018). (in press)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Mathematics and Applied Statistics, National Institute for Applied Statistics Research AustraliaUniversity of WollongongWollongongAustralia

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