  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.
