Non-Additive Random Data Perturbation for Real World Data

Privacy preservation is the major concern while real datasets are handled. A specific topic- privacy preserving data mining (PPDM), completely deals with data modification but also limits rule loss. Data perturbation is one of the PPDM techniques, which mostly deals with numerical data and concentrates on the statistical analysis of the data. Perturbation is of two types, additive perturbation and multiplicative perturbation, where generated random data is either added or multiplied with the data, which results in a random modified data. In this paper we have proposed a model in which the perturbation is done by randomization, where the data is generated in intervals based on the level of privacy specified by each customer. Our model is proved by applying classification algorithm on the perturbed data set and the accuracy is still maintained the same.