Noise Addition for Individual Records to Preserve Privacy and Statistical Characteristics: Case Study of Real Estate Transaction Data
This work addresses privacy preservation for real estate data users, but it is incremental as it builds on existing noise addition techniques with a specific parameter optimization.
The authors tackled the problem of preserving privacy in individual records while maintaining statistical characteristics, specifically for real estate transaction data, by proposing a noise addition method that does not affect regression analysis results and recommending a parameter value based on numerical experiments to balance perturbation and coherence.
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.