Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique

Abstract

We propose use of Latin Hypercube Sampling to create a synthetic data set that reproduces many of the essential features of an original data set while providing disclosure protection. The synthetic micro data can also be used to create either additive or multiplicative noise which when merged with the original data can provide disclosure protection. The technique can also be used to create hybrid micro data sets containing pre-determined mixtures of real and synthetic data. We demonstrate the basic properties of the synthetic data approach by applying the Latin Hypercube Sampling technique to a database supported a by the Energy Information Administration. The use of Latin Hypercube Sampling, along with the goal of reproducing the rank correlation structure instead of the Pearson correlation structure, has not been previously applied to the disclosure protection problem. Given its properties, this technique offers multiple alternatives to current methods for providing disclosure protection for large data sets.