Noise Addition for Individual Records to Preserve Privacy and Statistical Characteristics: Case Study of Real Estate Transaction Data

TL;DR

This paper introduces a noise addition method for real estate data that preserves statistical properties and regression results, balancing data privacy with data utility through a controllable parameter.

Contribution

It presents a novel noise perturbation technique that maintains regression analysis outcomes and offers a simple parameter to control the perturbation level.

Findings

Effective noise addition preserves regression results.

Recommended parameter balances privacy and data utility.

Numerical experiments validate the method's effectiveness.

Abstract

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.