Noise Addition for Individual Records to Preserve Privacy and Statistical Characteristics: Case Study of Real Estate Transaction Data
Yuzo Maruyama, Ryoko Tone, Yasushi Asami · arXiv (Cornell University) · 2015
We propose a new method of perturbing a major variable by adding noise such that results of regression analysis are unaffected. The extent of the perturbation can be controlled using a single parameter, which eases an actual perturbation application. On the basis of results of a numerical experiment, we recommend an appropriate value of the parameter that can achieve both sufficient perturbation to mask original values and sufficient coherence between perturbed and original data.