Recent Research Results on the Conditional Distribution Approach for Data Perturbation

Abstract

In this extended abstract, we provide a summary of our recent research on developing a theoretical basis for perturbation methods. We propose that, theoretically, generating perturbed values of the confidential variables from the conditional distribution of the confidential variables given the non-confidential variables, but independent of the original confidential variables. We show that if the perturbed values are generated from this approach, the resulting perturbed values have the same statistical characteristics as the original confidential variables, and maintain all relationships among the variables to be the same after perturbation as before perturbation. Furthermore, since given the nonconfidential variables, the perturbed variables are independent of the original confidential variables, this method also provides intruders with no knowledge gain. For a complete description, please see Muralidhar and Sarathy (2003). In the following sections, we describe our efforts in developing techniques based on this theoretical approach for numerical, confidential variables. Our initial effort focused on the desire to improve the performance of existing additive noise techniques for numerical, confidential variables. One of the key aspects of nois